model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bitcoin price prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "import tensorflow as tf\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import data\n",
    "https://www.investing.com/crypto/bitcoin/historical-data\n",
    "\n",
    "2010-07-18 - 10/24/2023\n",
    "\n",
    "Data: Daily Historical price of Bitcoin over the past 23 years\n",
    "\n",
    "Vol: the sum of all buy-sell transactions on a daily basis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>Change %</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-10-24</th>\n",
       "      <td>34,562.9</td>\n",
       "      <td>33,037.3</td>\n",
       "      <td>35,191.4</td>\n",
       "      <td>32,866.5</td>\n",
       "      <td>183.50K</td>\n",
       "      <td>4.60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-23</th>\n",
       "      <td>33,041.8</td>\n",
       "      <td>29,984.2</td>\n",
       "      <td>34,375.6</td>\n",
       "      <td>29,891.4</td>\n",
       "      <td>133.46K</td>\n",
       "      <td>10.20%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-22</th>\n",
       "      <td>29,984.2</td>\n",
       "      <td>29,912.7</td>\n",
       "      <td>30,193.4</td>\n",
       "      <td>29,713.6</td>\n",
       "      <td>29.07K</td>\n",
       "      <td>0.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-21</th>\n",
       "      <td>29,912.9</td>\n",
       "      <td>29,674.7</td>\n",
       "      <td>30,264.8</td>\n",
       "      <td>29,469.1</td>\n",
       "      <td>34.68K</td>\n",
       "      <td>0.80%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-20</th>\n",
       "      <td>29,674.5</td>\n",
       "      <td>28,724.0</td>\n",
       "      <td>30,165.9</td>\n",
       "      <td>28,586.1</td>\n",
       "      <td>82.68K</td>\n",
       "      <td>3.31%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Price      Open      High       Low     Vol. Change %\n",
       "Date                                                                \n",
       "2023-10-24  34,562.9  33,037.3  35,191.4  32,866.5  183.50K    4.60%\n",
       "2023-10-23  33,041.8  29,984.2  34,375.6  29,891.4  133.46K   10.20%\n",
       "2023-10-22  29,984.2  29,912.7  30,193.4  29,713.6   29.07K    0.24%\n",
       "2023-10-21  29,912.9  29,674.7  30,264.8  29,469.1   34.68K    0.80%\n",
       "2023-10-20  29,674.5  28,724.0  30,165.9  28,586.1   82.68K    3.31%"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df = pd.read_csv(\n",
    "    'Bitcoin Historical Data(1).csv',\n",
    "    parse_dates=['Date'],\n",
    "    index_col=['Date']\n",
    ")\n",
    "data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>Change %</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-07-22</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2.16K</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-21</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.58K</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-20</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.26K</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-19</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.57K</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-18</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.08K</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Price Open High  Low   Vol. Change %\n",
       "Date                                           \n",
       "2010-07-22   0.1  0.1  0.1  0.1  2.16K    0.00%\n",
       "2010-07-21   0.1  0.1  0.1  0.1  0.58K    0.00%\n",
       "2010-07-20   0.1  0.1  0.1  0.1  0.26K    0.00%\n",
       "2010-07-19   0.1  0.1  0.1  0.1  0.57K    0.00%\n",
       "2010-07-18   0.1  0.0  0.1  0.1  0.08K    0.00%"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>Change %</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-07-18</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.08K</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-19</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.57K</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-20</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.26K</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-21</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.58K</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-22</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2.16K</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Price Open High  Low   Vol. Change %\n",
       "Date                                           \n",
       "2010-07-18   0.1  0.0  0.1  0.1  0.08K    0.00%\n",
       "2010-07-19   0.1  0.1  0.1  0.1  0.57K    0.00%\n",
       "2010-07-20   0.1  0.1  0.1  0.1  0.26K    0.00%\n",
       "2010-07-21   0.1  0.1  0.1  0.1  0.58K    0.00%\n",
       "2010-07-22   0.1  0.1  0.1  0.1  2.16K    0.00%"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# order the data by date\n",
    "data_df.sort_index(inplace=True)\n",
    "data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 4847 entries, 2010-07-18 to 2023-10-24\n",
      "Data columns (total 6 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   Price     4847 non-null   object\n",
      " 1   Open      4847 non-null   object\n",
      " 2   High      4847 non-null   object\n",
      " 3   Low       4847 non-null   object\n",
      " 4   Vol.      4841 non-null   object\n",
      " 5   Change %  4847 non-null   object\n",
      "dtypes: object(6)\n",
      "memory usage: 265.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>Change %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4847</td>\n",
       "      <td>4847</td>\n",
       "      <td>4847</td>\n",
       "      <td>4847</td>\n",
       "      <td>4841</td>\n",
       "      <td>4847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3893</td>\n",
       "      <td>3885</td>\n",
       "      <td>3880</td>\n",
       "      <td>3890</td>\n",
       "      <td>4309</td>\n",
       "      <td>1537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.05M</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>97</td>\n",
       "      <td>101</td>\n",
       "      <td>6</td>\n",
       "      <td>428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       Price  Open  High   Low   Vol. Change %\n",
       "count   4847  4847  4847  4847   4841     4847\n",
       "unique  3893  3885  3880  3890   4309     1537\n",
       "top      0.1   0.1   0.1   0.1  1.05M    0.00%\n",
       "freq     100   100    97   101      6      428"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### preprocess data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_colon(number):\n",
    "    \"\"\"\n",
    "    remove column in the price value and convert it to float\n",
    "    example of number to convert: 34,562.9\n",
    "    args:\n",
    "        number: the number to convert\n",
    "    \"\"\"\n",
    "    new_number = ''\n",
    "    for i in number:\n",
    "        if i == ',':\n",
    "            continue\n",
    "        else:\n",
    "            new_number += i            \n",
    "    return float(new_number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df.Price = data_df.Price.apply(remove_colon)\n",
    "data_df.Open = data_df.Open.apply(remove_colon)\n",
    "data_df.High = data_df.High.apply(remove_colon)\n",
    "data_df.Low = data_df.Low.apply(remove_colon)\n",
    "# data_df['Vol.'] = data_df['Vol.'].apply(remove_k_and_add_000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price       float64\n",
       "Open        float64\n",
       "High        float64\n",
       "Low         float64\n",
       "Vol.         object\n",
       "Change %     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Date'>"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evolution of the price of bitcoin overtime\n",
    "\n",
    "timestamps_price_df = data_df[['Price']]\n",
    "timestamps_price_df.plot(figsize=(10, 7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split the data into train and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_size = int(0.8 * data_df.shape[0])\n",
    "\n",
    "# x_train and y_train\n",
    "x_train, y_train = timestamps_price_df.index[:split_size], timestamps_price_df.Price[:split_size]\n",
    "\n",
    "# x_test and y_test\n",
    "x_test, y_test = timestamps_price_df.index[split_size:], timestamps_price_df.Price[split_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3877,), (3877,), (970,), (970,))"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, y_train.shape, x_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "# visualize train and test data\n",
    "def plot_times_series(timesteps, values, format='.', start=0, end=None, label=None):\n",
    "    \"\"\"\n",
    "    Plots timesteps (a series of points in time) against values\n",
    "    args:\n",
    "    timesteps: array of timestep values\n",
    "    values: array of values accros time\n",
    "    format: style of plot, default = '.'\n",
    "    start: where to start the plot\n",
    "    end: where to end the plot\n",
    "    label: label to show on plot about values, default None\n",
    "    \"\"\"\n",
    "    plt.plot(timesteps[start:end], values[start:end], format, label=label)\n",
    "    plt.xlabel('Time')\n",
    "    plt.ylabel('BTC Price')\n",
    "    if label:\n",
    "        plt.legend(fontsize=14)\n",
    "    plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "plot_times_series(timesteps=x_train, values=y_train, label='Train data')\n",
    "plot_times_series(timesteps=x_test, values=y_test, label='Test data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Modeling\n",
    "\n",
    "    - Horizon = number of timesteps into the future we are going to predict\n",
    "    - Window size = number of timesteps er are going to use to predict horizon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model 0: Naive forecast (baseline)\n",
    "\n",
    "> The prediction at timestep t is equal to the value at timestep t-1 (previous) timestep (this for horizon=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MASE implementation\n",
    "def mean_absolute_scaled_error(y_true, y_pred):\n",
    "    \"\"\"\n",
    "    Implementation MASE (assuming no seasonality of data)\n",
    "    \"\"\"\n",
    "    mae = tf.reduce_mean(tf.abs(y_true, y_pred))\n",
    "    \n",
    "    mae_naive_no_season = tf.reduce_mean(tf.abs(y_true[1:] - y_true[:-1]))\n",
    "    \n",
    "    return mae / mae_naive_no_season\n",
    "\n",
    "def evaluate_predictions(y_true, y_pred):\n",
    "    # Make sur float32 datatype (for metric calculations)\n",
    "    y_true = tf.cast(y_true, dtype=tf.float32)\n",
    "    y_pred = tf.cast(y_pred, dtype=tf.float32)\n",
    "    \n",
    "    # Calculate various evaluation metrics\n",
    "    mae = tf.keras.metrics.mean_absolute_error(y_true, y_pred)\n",
    "    mse = tf.keras.metrics.mean_squared_error(y_true, y_pred)\n",
    "    rmse = tf.sqrt(mse)\n",
    "    mape = tf.keras.metrics.mean_absolute_percentage_error(y_true, y_pred)\n",
    "    mase = mean_absolute_scaled_error(y_true, y_pred)\n",
    "    \n",
    "    # Account for different sized metrics(for longer horizon)\n",
    "    if mae.ndim > 0:\n",
    "        mae = tf.reduce_mean(mae)\n",
    "        mse = tf.reduce_mean(mse)\n",
    "        rmse = tf.reduce_mean(rmse)\n",
    "        mape = tf.reduce_mean(mape)\n",
    "        mase = tf.reduce_mean(mase)\n",
    "    return {\n",
    "        'mae': mae.numpy(),\n",
    "        'mse': mse.numpy(),\n",
    "        'rmse': rmse.numpy(),\n",
    "        'mape': mape.numpy(),\n",
    "        'mase': mase.numpy() \n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Windowing our dataset\n",
    "Window time series dataset to turn it into a supervised learning problem.\n",
    "\n",
    "```\n",
    "[0, 1, 2, 3, 4, 5, 6] -> [7]\n",
    "[1, 2, 3, 4, 5, 6, 7] -> [8]\n",
    "[2, 3, 4, 5, 6, 7, 8] -> [9]\n",
    "```\n",
    "\n",
    "tf.keras.preprocessing.timeseries_dataset_from_array()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "HORIZON = 1\n",
    "WINDOW_SISE = 7\n",
    "\n",
    "def get_labelled_window(x, horizon=HORIZON):\n",
    "    \"\"\"\n",
    "    create labels for windowed dataset\n",
    "    \n",
    "    eg: for horizon=1\n",
    "    Input: [0, 1, 2, 3, 4, 5, 6, 7] -> Output: ([0, 1, 2, 3, 4, 5, 6], [7])\n",
    "    \"\"\"\n",
    "    return x[:, :-horizon], x[:, -horizon:]\n",
    "\n",
    "def make_windows(x, window_size=WINDOW_SISE, horizon=HORIZON):\n",
    "    \"\"\"\n",
    "    Turn a 1D array into a 2D array of sequential labelled windows of window_size with horizon size labels\n",
    "    \"\"\"\n",
    "    # create a window of specific window_size (add howizon on the end for labelling later)\n",
    "    window_step = np.expand_dims(np.arange(window_size+horizon), axis=0)\n",
    "    # create a 2D array of multiple window steps (minus 1 to account for 0 indexing)\n",
    "    window_indexes = window_step + np.expand_dims(np.arange(len(x) - (window_size + horizon-1)), axis=0).T   \n",
    "    # index on the target array (a time series) with 2D array of multiple window steps\n",
    "    windowed_array = x[window_indexes]\n",
    "    # get the labelled windows\n",
    "    windows, labels = get_labelled_window(windowed_array, horizon=horizon)\n",
    "    return windows, labels\n",
    "\n",
    "def make_train_test_splits(windows, labels, test_split=0.2):\n",
    "    \"\"\"\n",
    "    Splits matching pairs of windows and labels into train and test splits\n",
    "    \"\"\"\n",
    "    split_size = int(len(windows) * (1-test_split))\n",
    "    train_windows = windows[:split_size]\n",
    "    train_labels = labels[:split_size]\n",
    "    test_windows = windows[split_size:]\n",
    "    test_labels = labels[split_size:]\n",
    "    return train_windows, test_windows, train_labels, test_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_windows, full_labels = make_windows(timestamps_price_df.Price.values, WINDOW_SISE, HORIZON)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3872, 968, 3872, 968)"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_windows, test_windows, train_labels, test_labels = make_train_test_splits(full_windows, full_labels)\n",
    "len(train_windows), len(test_windows), len(train_labels), len(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to save the model where the val loss is the lowest as the model fluctuate\n",
    "def create_model_checkpoint(model_name, save_path='model_experiments'):\n",
    "    return tf.keras.callbacks.ModelCheckpoint(filepath=os.path.join(save_path, model_name),\n",
    "                                              verbose=0,\n",
    "                                              save_best_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model 1: Dense model (window = 7, horizon = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set random seed for as reproductible results as possible\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model_1 = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dense(HORIZON, activation='linear')\n",
    "], name='model_1')\n",
    "\n",
    "model_1.compile(\n",
    "    loss='mae',\n",
    "    optimizer=tf.keras.optimizers.Adam(),\n",
    "    metrics=['mae', 'mse']\n",
    ")\n",
    "\n",
    "history_1 = model_1.fit(\n",
    "    x=train_windows,\n",
    "    y=train_labels,\n",
    "    epochs=100,\n",
    "    verbose=0,\n",
    "    batch_size=128,\n",
    "    validation_data=(test_windows, test_labels),\n",
    "    callbacks=[create_model_checkpoint(model_name=model_1.name)]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_1_loaded = tf.keras.models.load_model('model_experiments/model_1/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31/31 [==============================] - 0s 3ms/step - loss: 819.4719 - mae: 819.4719 - mse: 1620772.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[819.4718627929688, 819.4718627929688, 1620772.0]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1_loaded.evaluate(test_windows, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_preds(model, input_data):\n",
    "    \"\"\"\n",
    "    Use model to make predictions input_data\n",
    "    \"\"\"\n",
    "    forecast = model.predict(input_data)\n",
    "    \n",
    "    return  tf.squeeze(forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31/31 [==============================] - 0s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "model_1_loaded_preds = make_preds(model_1_loaded, test_windows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_1_results = evaluate_predictions(tf.squeeze(test_labels), model_1_loaded_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mae': 819.472,\n",
       " 'mse': 1620771.8,\n",
       " 'rmse': 1273.0953,\n",
       " 'mape': 2.2836134,\n",
       " 'mase': 42.008}"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "offset = 450\n",
    "plt.figure(figsize=(10, 7))\n",
    "plot_times_series(timesteps=x_test[-len(test_windows):],\n",
    "                  values=test_labels[:, 0],\n",
    "                  start=offset,\n",
    "                  label='Test data')\n",
    "\n",
    "plot_times_series(timesteps=x_test[-len(test_windows):],\n",
    "                  values=model_1_loaded_preds,\n",
    "                  start=offset,\n",
    "                  format='-',\n",
    "                  label='model 1 preds data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conv 1D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "# expand dimension\n",
    "expand_dims_layer = tf.keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.random.set_seed(42)\n",
    "model_2 = tf.keras.Sequential([\n",
    "    tf.keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=1)),\n",
    "    tf.keras.layers.Conv1D(filters=128, kernel_size=5, strides=1, padding='causal', activation='relu'),\n",
    "    tf.keras.layers.Dense(HORIZON)\n",
    "], name='model_2_conv1D')\n",
    "\n",
    "model_2.compile(\n",
    "    loss='mae',\n",
    "    optimizer=tf.keras.optimizers.Adam(),\n",
    "    metrics=['mae', 'mse']\n",
    ")\n",
    "\n",
    "history_2 = model_2.fit(\n",
    "    x=train_windows,\n",
    "    y=train_labels,\n",
    "    epochs=100,\n",
    "    verbose=0,\n",
    "    batch_size=128,\n",
    "    validation_data=(test_windows, test_labels),\n",
    "    callbacks=[create_model_checkpoint(model_name=model_2.name)]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31/31 [==============================] - 0s 2ms/step - loss: 823.6212 - mae: 823.6212 - mse: 1640675.1250\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[823.6212158203125, 823.6212158203125, 1640675.125]"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2_loaded = tf.keras.models.load_model('model_experiments/model_2_conv1D/')\n",
    "model_2_loaded.evaluate(test_windows, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31/31 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "model_2_preds = make_preds(model_2_loaded, test_windows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mae': 14683.485,\n",
       " 'mse': 340888350.0,\n",
       " 'rmse': 17884.562,\n",
       " 'mape': 47.56548,\n",
       " 'mase': 42.008}"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2_res = evaluate_predictions(test_labels, model_2_preds)\n",
    "model_2_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multivariate time series dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>Change %</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-07-18</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.08K</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-19</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.57K</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-20</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.26K</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-21</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.58K</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-22</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2.16K</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Price  Open  High  Low   Vol. Change %\n",
       "Date                                              \n",
       "2010-07-18    0.1   0.0   0.1  0.1  0.08K    0.00%\n",
       "2010-07-19    0.1   0.1   0.1  0.1  0.57K    0.00%\n",
       "2010-07-20    0.1   0.1   0.1  0.1  0.26K    0.00%\n",
       "2010-07-21    0.1   0.1   0.1  0.1  0.58K    0.00%\n",
       "2010-07-22    0.1   0.1   0.1  0.1  2.16K    0.00%"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-07-18</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-19</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-20</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-21</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-22</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Price  High  Low\n",
       "Date                        \n",
       "2010-07-18    0.1   0.1  0.1\n",
       "2010-07-19    0.1   0.1  0.1\n",
       "2010-07-20    0.1   0.1  0.1\n",
       "2010-07-21    0.1   0.1  0.1\n",
       "2010-07-22    0.1   0.1  0.1"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timestamps_price_high_low_df = data_df[['Price', 'High', 'Low']]\n",
    "timestamps_price_high_low_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Date'>"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "timestamps_price_high_low_df.plot(figsize=(10, 7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Price+1</th>\n",
       "      <th>High+1</th>\n",
       "      <th>Price+2</th>\n",
       "      <th>High+2</th>\n",
       "      <th>Price+3</th>\n",
       "      <th>High+3</th>\n",
       "      <th>Price+4</th>\n",
       "      <th>High+4</th>\n",
       "      <th>Price+5</th>\n",
       "      <th>High+5</th>\n",
       "      <th>Price+6</th>\n",
       "      <th>High+6</th>\n",
       "      <th>Price+7</th>\n",
       "      <th>High+7</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-07-18</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-19</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-20</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-21</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-22</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-23</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-24</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-25</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-26</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-27</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Price  High  Low  Price+1  High+1  Price+2  High+2  Price+3  \\\n",
       "Date                                                                      \n",
       "2010-07-18    0.1   0.1  0.1      NaN     NaN      NaN     NaN      NaN   \n",
       "2010-07-19    0.1   0.1  0.1      0.1     0.1      NaN     NaN      NaN   \n",
       "2010-07-20    0.1   0.1  0.1      0.1     0.1      0.1     0.1      NaN   \n",
       "2010-07-21    0.1   0.1  0.1      0.1     0.1      0.1     0.1      0.1   \n",
       "2010-07-22    0.1   0.1  0.1      0.1     0.1      0.1     0.1      0.1   \n",
       "2010-07-23    0.1   0.1  0.1      0.1     0.1      0.1     0.1      0.1   \n",
       "2010-07-24    0.1   0.1  0.1      0.1     0.1      0.1     0.1      0.1   \n",
       "2010-07-25    0.1   0.1  0.1      0.1     0.1      0.1     0.1      0.1   \n",
       "2010-07-26    0.1   0.1  0.1      0.1     0.1      0.1     0.1      0.1   \n",
       "2010-07-27    0.1   0.1  0.1      0.1     0.1      0.1     0.1      0.1   \n",
       "\n",
       "            High+3  Price+4  High+4  Price+5  High+5  Price+6  High+6  \\\n",
       "Date                                                                    \n",
       "2010-07-18     NaN      NaN     NaN      NaN     NaN      NaN     NaN   \n",
       "2010-07-19     NaN      NaN     NaN      NaN     NaN      NaN     NaN   \n",
       "2010-07-20     NaN      NaN     NaN      NaN     NaN      NaN     NaN   \n",
       "2010-07-21     0.1      NaN     NaN      NaN     NaN      NaN     NaN   \n",
       "2010-07-22     0.1      0.1     0.1      NaN     NaN      NaN     NaN   \n",
       "2010-07-23     0.1      0.1     0.1      0.1     0.1      NaN     NaN   \n",
       "2010-07-24     0.1      0.1     0.1      0.1     0.1      0.1     0.1   \n",
       "2010-07-25     0.1      0.1     0.1      0.1     0.1      0.1     0.1   \n",
       "2010-07-26     0.1      0.1     0.1      0.1     0.1      0.1     0.1   \n",
       "2010-07-27     0.1      0.1     0.1      0.1     0.1      0.1     0.1   \n",
       "\n",
       "            Price+7  High+7  \n",
       "Date                         \n",
       "2010-07-18      NaN     NaN  \n",
       "2010-07-19      NaN     NaN  \n",
       "2010-07-20      NaN     NaN  \n",
       "2010-07-21      NaN     NaN  \n",
       "2010-07-22      NaN     NaN  \n",
       "2010-07-23      NaN     NaN  \n",
       "2010-07-24      NaN     NaN  \n",
       "2010-07-25      0.1     0.1  \n",
       "2010-07-26      0.1     0.1  \n",
       "2010-07-27      0.1     0.1  "
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# making a windowed dataset with pandas.\n",
    "bitcoin_prices_windowed = timestamps_price_high_low_df.copy()\n",
    "\n",
    "# Add windowed columns\n",
    "for i in range(WINDOW_SISE):\n",
    "    bitcoin_prices_windowed[[f'Price+{i+1}', f'High+{i+1}']] = bitcoin_prices_windowed[['Price', 'High']].shift(periods=i+1)\n",
    "    \n",
    "bitcoin_prices_windowed.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = bitcoin_prices_windowed.dropna().drop('Price', axis=1).astype(np.float32)\n",
    "y = bitcoin_prices_windowed.dropna()['Price'].astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4840, 16), (4840,))"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3872, 968, 3872, 968)"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make train and test sets using indexing\n",
    "train_windows, test_windows, train_labels, test_labels = make_train_test_splits(x, y)\n",
    "len(train_windows), len(test_windows), len(train_labels), len(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set random seed for as reproductible results as possible\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model_3 = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dense(HORIZON)\n",
    "], name='model_3')\n",
    "\n",
    "model_3.compile(\n",
    "    loss='mae',\n",
    "    optimizer=tf.keras.optimizers.Adam(),\n",
    "    metrics=['mae', 'mse']\n",
    ")\n",
    "\n",
    "history_3 = model_3.fit(\n",
    "    x=train_windows,\n",
    "    y=train_labels,\n",
    "    epochs=100,\n",
    "    verbose=0,\n",
    "    batch_size=128,\n",
    "    validation_data=(test_windows, test_labels),\n",
    "    callbacks=[create_model_checkpoint(model_name=model_3.name)]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31/31 [==============================] - 0s 5ms/step - loss: 416.7764 - mae: 416.7764 - mse: 364024.9062\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[416.7763671875, 416.7763671875, 364024.90625]"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_3_loaded = tf.keras.models.load_model('model_experiments/model_3')\n",
    "model_3_loaded.evaluate(test_windows, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31/31 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "# Make prediction\n",
    "model_3_preds = make_preds(model_3_loaded, test_windows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mae': 416.77634,\n",
       " 'mse': 364025.0,\n",
       " 'rmse': 603.34485,\n",
       " 'mape': 1.1653488,\n",
       " 'mase': 42.008}"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_3_res = evaluate_predictions(test_labels, model_3_preds)\n",
    "model_3_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### N-BEATS algorithm\n",
    "https://arxiv.org/pdf/1905.10437.pdf\n",
    "\n",
    "N-BEATS: NEURAL BASIS EXPANSION ANALYSIS FOR\n",
    "INTERPRETABLE TIME SERIES FORECASTING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Because the N-BEATS block layer does'nt exist in tensorflow, we've got to create it \n",
    "#To create custom layers and models in Tensorflow we can use subclassing\n",
    "\n",
    "# create the NBeatBlock custom layer\n",
    "class NBeatsBlock(tf.keras.layers.Layer):\n",
    "    def __init__(self,\n",
    "                  input_size: int,\n",
    "                  theta_size: int,\n",
    "                  horizon: int,\n",
    "                  n_neurons: int,\n",
    "                  n_layers: int,\n",
    "                  **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.input_size = input_size\n",
    "        self.theta_size = theta_size\n",
    "        self.horizon = horizon\n",
    "        self.n_neurons = n_neurons\n",
    "        self.n_layers = n_layers\n",
    "        \n",
    "        # Block contains stack of 4 fully connected layers each has ReLU activation\n",
    "        self.hidden = [tf.keras.layers.Dense(n_neurons, activation='relu') for _ in range(n_layers)]\n",
    "        # output of block is a theta layer with linear activation\n",
    "        self.theta_layer = tf.keras.layers.Dense(theta_size, activation='linear')\n",
    "        \n",
    "    def call(self, inputs):\n",
    "        x = inputs\n",
    "        for layer in self.hidden:\n",
    "            x = layer(x)\n",
    "        self.theta = self.theta_layer(x)\n",
    "        # output the backcast and the forcast from theta\n",
    "        backcast, forecast = self.theta[:, :self.input_size], self.theta[:, -self.horizon:]\n",
    "        return backcast, forecast\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-07-18</th>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-19</th>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-20</th>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-21</th>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-22</th>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Price\n",
       "Date             \n",
       "2010-07-18    0.1\n",
       "2010-07-19    0.1\n",
       "2010-07-20    0.1\n",
       "2010-07-21    0.1\n",
       "2010-07-22    0.1"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prepare data for the N-BEATS algorithm using 'tf.data' to make the input data load as fast as possible\n",
    "\n",
    "timestamps_price_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Price+1</th>\n",
       "      <th>Price+2</th>\n",
       "      <th>Price+3</th>\n",
       "      <th>Price+4</th>\n",
       "      <th>Price+5</th>\n",
       "      <th>Price+6</th>\n",
       "      <th>Price+7</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-07-18</th>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-19</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-20</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-21</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-22</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Price  Price+1  Price+2  Price+3  Price+4  Price+5  Price+6  \\\n",
       "Date                                                                      \n",
       "2010-07-18    0.1      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2010-07-19    0.1      0.1      NaN      NaN      NaN      NaN      NaN   \n",
       "2010-07-20    0.1      0.1      0.1      NaN      NaN      NaN      NaN   \n",
       "2010-07-21    0.1      0.1      0.1      0.1      NaN      NaN      NaN   \n",
       "2010-07-22    0.1      0.1      0.1      0.1      0.1      NaN      NaN   \n",
       "\n",
       "            Price+7  \n",
       "Date                 \n",
       "2010-07-18      NaN  \n",
       "2010-07-19      NaN  \n",
       "2010-07-20      NaN  \n",
       "2010-07-21      NaN  \n",
       "2010-07-22      NaN  "
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add windowed column\n",
    "bitcoin_prices_nbeats = timestamps_price_df.copy()\n",
    "\n",
    "for i in range(WINDOW_SISE):\n",
    "    bitcoin_prices_nbeats[f'Price+{i+1}'] = bitcoin_prices_nbeats['Price'].shift(periods=i+1)\n",
    "    \n",
    "bitcoin_prices_nbeats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3872, 968, 3872, 968)"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make features and labels\n",
    "x = bitcoin_prices_nbeats.dropna().drop('Price', axis=1)\n",
    "y = bitcoin_prices_nbeats.dropna()['Price']\n",
    "\n",
    "# Make train and test sets using indexing\n",
    "train_windows, test_windows, train_labels, test_labels = make_train_test_splits(x, y)\n",
    "len(train_windows), len(test_windows), len(train_labels), len(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price+1</th>\n",
       "      <th>Price+2</th>\n",
       "      <th>Price+3</th>\n",
       "      <th>Price+4</th>\n",
       "      <th>Price+5</th>\n",
       "      <th>Price+6</th>\n",
       "      <th>Price+7</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-07-25</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-26</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-27</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-28</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-29</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-20</th>\n",
       "      <td>28723.0</td>\n",
       "      <td>28321.7</td>\n",
       "      <td>28403.6</td>\n",
       "      <td>28509.7</td>\n",
       "      <td>27161.2</td>\n",
       "      <td>26852.8</td>\n",
       "      <td>26863.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-21</th>\n",
       "      <td>29674.5</td>\n",
       "      <td>28723.0</td>\n",
       "      <td>28321.7</td>\n",
       "      <td>28403.6</td>\n",
       "      <td>28509.7</td>\n",
       "      <td>27161.2</td>\n",
       "      <td>26852.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-22</th>\n",
       "      <td>29912.9</td>\n",
       "      <td>29674.5</td>\n",
       "      <td>28723.0</td>\n",
       "      <td>28321.7</td>\n",
       "      <td>28403.6</td>\n",
       "      <td>28509.7</td>\n",
       "      <td>27161.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-23</th>\n",
       "      <td>29984.2</td>\n",
       "      <td>29912.9</td>\n",
       "      <td>29674.5</td>\n",
       "      <td>28723.0</td>\n",
       "      <td>28321.7</td>\n",
       "      <td>28403.6</td>\n",
       "      <td>28509.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-24</th>\n",
       "      <td>33041.8</td>\n",
       "      <td>29984.2</td>\n",
       "      <td>29912.9</td>\n",
       "      <td>29674.5</td>\n",
       "      <td>28723.0</td>\n",
       "      <td>28321.7</td>\n",
       "      <td>28403.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4840 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Price+1  Price+2  Price+3  Price+4  Price+5  Price+6  Price+7\n",
       "Date                                                                     \n",
       "2010-07-25      0.1      0.1      0.1      0.1      0.1      0.1      0.1\n",
       "2010-07-26      0.1      0.1      0.1      0.1      0.1      0.1      0.1\n",
       "2010-07-27      0.1      0.1      0.1      0.1      0.1      0.1      0.1\n",
       "2010-07-28      0.1      0.1      0.1      0.1      0.1      0.1      0.1\n",
       "2010-07-29      0.1      0.1      0.1      0.1      0.1      0.1      0.1\n",
       "...             ...      ...      ...      ...      ...      ...      ...\n",
       "2023-10-20  28723.0  28321.7  28403.6  28509.7  27161.2  26852.8  26863.5\n",
       "2023-10-21  29674.5  28723.0  28321.7  28403.6  28509.7  27161.2  26852.8\n",
       "2023-10-22  29912.9  29674.5  28723.0  28321.7  28403.6  28509.7  27161.2\n",
       "2023-10-23  29984.2  29912.9  29674.5  28723.0  28321.7  28403.6  28509.7\n",
       "2023-10-24  33041.8  29984.2  29912.9  29674.5  28723.0  28321.7  28403.6\n",
       "\n",
       "[4840 rows x 7 columns]"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TIme to make our dataset performant using tf.data API\n",
    "train_features_dataset = tf.data.Dataset.from_tensor_slices(train_windows)\n",
    "train_labels_dataset = tf.data.Dataset.from_tensor_slices(train_labels)\n",
    "\n",
    "test_features_dataset = tf.data.Dataset.from_tensor_slices(test_windows)\n",
    "test_labels_dataset = tf.data.Dataset.from_tensor_slices(test_labels)\n",
    "\n",
    "# combine labels and features by zipping together (features, labels)\n",
    "train_dataset = tf.data.Dataset.zip((train_features_dataset, train_labels_dataset))\n",
    "test_dataset = tf.data.Dataset.zip((test_features_dataset, test_labels_dataset))\n",
    "\n",
    "# Batching and prefech\n",
    "BATCH_SIZE = 1024\n",
    "train_dataset = train_dataset.batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)\n",
    "test_dataset = test_dataset.batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up hyperparameters\n",
    "N_EPOCHS = 5000 # in the paper refers to iterations\n",
    "N_NEURONS = 512 # in the paper refers to width\n",
    "N_LAYERS = 4\n",
    "N_STACKS = 30\n",
    "INPUT_SIZE = WINDOW_SISE * HORIZON\n",
    "THETA_SIZE = INPUT_SIZE + HORIZON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get ready the residual connection\n",
    "# Before we can craft N-BEATS model, we need 2 layers for the residual connections (substract and add)\n",
    "# The N-BEATS algorithm uses double residual stacking to help train its deeper architechture\n",
    "tensor_1 = tf.range(10) + 10\n",
    "tensor_2 = tf.range(10)\n",
    "# substract\n",
    "substracted = tf.keras.layers.subtract([tensor_1, tensor_2])\n",
    "# add\n",
    "added = tf.keras.layers.add([tensor_1, tensor_2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building, compiling and fitting the N-BEATS algo\n",
    "\n",
    "1- Setup an instance of the N-BEATS Block layer using `NBeatsBlock` class\n",
    "\n",
    "2- Create an input layer for the N-BEATS stack (using functional API)\n",
    "\n",
    "3- Make the initial backcast and forecasts for the model with the layer created in 1\n",
    "\n",
    "4 - Use for loop to create stacks of block layers\n",
    "\n",
    "5- Use the `NBeatsBlock` class within the for loop in 4 to create blocks which return backcasts and block-level forecasts\n",
    "\n",
    "6- Create the double residual stacking using snsract and add layers\n",
    "\n",
    "7- Put the model inputs and outputs together using `tf.keras.Model()`\n",
    "\n",
    "8- Compile the model\n",
    "\n",
    "9- Fit model for 5k\n",
    "    callbacks: \n",
    "        - Early stopping: (stop if the model stop improving)\n",
    "        - Reduce LR on plateau: if the model stops improving, try lowering the learning rate to reduce the amounts it updates its weights each time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.random.set_seed(42)\n",
    "\n",
    "# 1- Setup and instance of NBeatsBlock\n",
    "nbeats_block_layer = NBeatsBlock(input_size=INPUT_SIZE,\n",
    "                                 theta_size=THETA_SIZE,\n",
    "                                 horizon=HORIZON,\n",
    "                                 n_neurons=N_NEURONS,\n",
    "                                 n_layers=N_LAYERS,\n",
    "                                 name='InitialBlock')\n",
    "# 2- Create an input layer for the N-BEATS stack (using functional API)\n",
    "stack_input = tf.keras.layers.Input(shape=(INPUT_SIZE), name='stack_input')\n",
    "\n",
    "# 3- Make the initial backcast and forecasts for the model with the layer created in 1\n",
    "residuals, forecast = nbeats_block_layer(stack_input)\n",
    "\n",
    "# 4 - Use for loop to create stacks of block layers\n",
    "for i, _ in enumerate(range(N_STACKS-1)):\n",
    "    # 5- Use the `NBeatsBlock` class within the for loop in 4 to create blocks which return backcasts and block-level forecasts\n",
    "    backcast, block_forecast = NBeatsBlock(input_size=INPUT_SIZE,\n",
    "                                 theta_size=THETA_SIZE,\n",
    "                                 horizon=HORIZON,\n",
    "                                 n_neurons=N_NEURONS,\n",
    "                                 n_layers=N_LAYERS,\n",
    "                                 name=f'NBeatsBlock_{i}')(residuals) # pass in the residuals\n",
    "    # 6- create the double residual stacking\n",
    "    residuals = tf.keras.layers.subtract([residuals, backcast], name=f'substract_{i}')\n",
    "    forecast = tf.keras.layers.add([forecast, block_forecast], name=f'add_{i}')\n",
    "\n",
    "# 7- Put the model inputs and outputs together using `tf.keras.Model()`\n",
    "nbeats_model = tf.keras.Model(inputs=stack_input, outputs=forecast, name='NBeatsModel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 555: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "\n",
      "Epoch 1030: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "\n",
      "Epoch 1130: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n"
     ]
    }
   ],
   "source": [
    "nbeats_model.compile(\n",
    "    loss='mae',\n",
    "    optimizer=tf.keras.optimizers.Adam(),\n",
    "    metrics=['mae', 'mse']\n",
    ")\n",
    "\n",
    "history_4 = nbeats_model.fit(\n",
    "    train_dataset,\n",
    "    epochs=5000,\n",
    "    verbose=0,\n",
    "    batch_size=128,\n",
    "    validation_data=test_dataset,\n",
    "    callbacks=[#create_model_checkpoint(model_name=nbeats_model.name),\n",
    "               tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=200, restore_best_weights=True), # stop if the model is not improving on the val loss in 5 epochs and restore best performing weight\n",
    "               tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', patience=100, verbose=1)] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 387ms/step - loss: 826.5875 - mae: 826.5875 - mse: 1630284.5000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[826.5874633789062, 826.5874633789062, 1630284.5]"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbeats_model.evaluate(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 3s 3s/step\n"
     ]
    }
   ],
   "source": [
    "# Make predictions\n",
    "nbeats_model_preds = make_preds(nbeats_model, test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mae': 826.58746,\n",
       " 'mse': 1630284.5,\n",
       " 'rmse': 1276.8259,\n",
       " 'mape': 2.2994082,\n",
       " 'mase': 42.008}"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbeats_res = evaluate_predictions(test_labels, nbeats_model_preds)\n",
    "nbeats_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating an Ensemble (stacking different models together)\n",
    "\n",
    "Combine different models to make a decision\n",
    "\n",
    "fit on 3 differents metrics with different random initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "# construct and fitting and ensemble of models (using different loss functions)\n",
    "def get_ensemble_models(horizon=HORIZON, \n",
    "                        train_data=train_dataset, \n",
    "                        test_data=test_dataset, \n",
    "                        num_iter=5,\n",
    "                        num_epochs=1000,\n",
    "                        loss_fn=['mae', 'mse', 'mape']):\n",
    "    \"\"\"\n",
    "    Returns a list of num_iter models each trained on MAE, MSE, and MAPE loss.\n",
    "    For example if num_iter=10, a list of 30 trained models will be retruned:\n",
    "    10 * len(['mae', 'mse', 'mape'])\n",
    "    \"\"\"\n",
    "    # make empty lsit for trained ensemble models \n",
    "    ensemble_models = []\n",
    "    \n",
    "    # create num_iter number of models per loss function\n",
    "    for i in range(num_iter):\n",
    "        # Build and fit a new model with a different loss function\n",
    "        for loss_function in loss_fn:\n",
    "            print(f'Optimizing model by reducing: {loss_function} for {num_epochs} epochs, model number: {i}')\n",
    "            \n",
    "            # construct a simple model\n",
    "            model = tf.keras.Sequential([\n",
    "                tf.keras.layers.Dense(128, activation='relu', kernel_initializer='he_normal'),\n",
    "                tf.keras.layers.Dense(HORIZON, activation='linear')\n",
    "            ], name=f'model_{i}')\n",
    "\n",
    "            model.compile(\n",
    "                loss=loss_function,\n",
    "                optimizer=tf.keras.optimizers.Adam(),\n",
    "                metrics=['mae', 'mse']\n",
    "            )\n",
    "\n",
    "            model.fit(\n",
    "                train_data,\n",
    "                epochs=num_epochs,\n",
    "                verbose=0,\n",
    "                batch_size=128,\n",
    "                validation_data=test_data,\n",
    "                callbacks=[\n",
    "                    # create_model_checkpoint(model_name=model.name)\n",
    "                    tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=200, restore_best_weights=True), # stop if the model is not improving on the val loss in 5 epochs and restore best performing weight\n",
    "                    tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', patience=100, verbose=1)\n",
    "                ]\n",
    "            )\n",
    "            ensemble_models.append(model)\n",
    "    return ensemble_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizing model by reducing: mae for 1000 epochs, model number: 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 705: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "\n",
      "Epoch 812: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "\n",
      "Epoch 912: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n",
      "Optimizing model by reducing: mse for 1000 epochs, model number: 0\n",
      "\n",
      "Epoch 292: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "\n",
      "Epoch 392: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "Optimizing model by reducing: mape for 1000 epochs, model number: 0\n",
      "\n",
      "Epoch 702: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "Optimizing model by reducing: mae for 1000 epochs, model number: 1\n",
      "Optimizing model by reducing: mse for 1000 epochs, model number: 1\n",
      "\n",
      "Epoch 919: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "Optimizing model by reducing: mape for 1000 epochs, model number: 1\n",
      "\n",
      "Epoch 639: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "Optimizing model by reducing: mae for 1000 epochs, model number: 2\n",
      "\n",
      "Epoch 690: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "\n",
      "Epoch 797: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "\n",
      "Epoch 897: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n",
      "Optimizing model by reducing: mse for 1000 epochs, model number: 2\n",
      "\n",
      "Epoch 236: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "\n",
      "Epoch 336: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "Optimizing model by reducing: mape for 1000 epochs, model number: 2\n",
      "\n",
      "Epoch 878: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "Optimizing model by reducing: mae for 1000 epochs, model number: 3\n",
      "\n",
      "Epoch 292: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "\n",
      "Epoch 398: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "\n",
      "Epoch 498: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n",
      "Optimizing model by reducing: mse for 1000 epochs, model number: 3\n",
      "\n",
      "Epoch 304: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "\n",
      "Epoch 404: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "Optimizing model by reducing: mape for 1000 epochs, model number: 3\n",
      "\n",
      "Epoch 712: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "\n",
      "Epoch 961: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "Optimizing model by reducing: mae for 1000 epochs, model number: 4\n",
      "\n",
      "Epoch 532: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "Optimizing model by reducing: mse for 1000 epochs, model number: 4\n",
      "\n",
      "Epoch 717: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "\n",
      "Epoch 817: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "Optimizing model by reducing: mape for 1000 epochs, model number: 4\n",
      "\n",
      "Epoch 609: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n"
     ]
    }
   ],
   "source": [
    "ensemble_models = get_ensemble_models()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a function which uses a list of trained models to make and return a lsit of predictions\n",
    "def make_ens_preds(ensemble_models=ensemble_models, test_data=test_dataset):\n",
    "    ensemble_preds = []\n",
    "    for model in ensemble_models:\n",
    "        preds = model.predict(test_data)\n",
    "        ensemble_preds.append(preds)\n",
    "    return tf.constant(tf.squeeze(ensemble_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 52ms/step\n",
      "1/1 [==============================] - 0s 66ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 61ms/step\n",
      "1/1 [==============================] - 0s 53ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 53ms/step\n",
      "1/1 [==============================] - 0s 100ms/step\n",
      "1/1 [==============================] - 0s 67ms/step\n",
      "1/1 [==============================] - 0s 73ms/step\n",
      "1/1 [==============================] - 0s 130ms/step\n",
      "1/1 [==============================] - 0s 90ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 63ms/step\n",
      "1/1 [==============================] - 0s 90ms/step\n",
      "1/1 [==============================] - 0s 125ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(15, 968), dtype=float32, numpy=\n",
       "array([[45671.926, 49384.785, 48156.2  , ..., 29999.328, 30107.633,\n",
       "        33246.453],\n",
       "       [44711.81 , 49431.16 , 48315.793, ..., 30300.922, 30047.023,\n",
       "        33301.81 ],\n",
       "       [45331.895, 49573.176, 48612.527, ..., 30108.379, 30120.828,\n",
       "        33109.293],\n",
       "       ...,\n",
       "       [45184.934, 49218.168, 48344.996, ..., 29985.953, 30143.527,\n",
       "        32975.715],\n",
       "       [44603.63 , 48555.57 , 47333.53 , ..., 30402.354, 30301.607,\n",
       "        33392.21 ],\n",
       "       [45039.53 , 49770.2  , 48551.18 , ..., 30116.896, 30142.691,\n",
       "        33199.453]], dtype=float32)>"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ensemble_preds = make_ens_preds()\n",
    "ensemble_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mae': 828.0799,\n",
       " 'mse': 1627459.6,\n",
       " 'rmse': 1275.7192,\n",
       " 'mape': 2.3069751,\n",
       " 'mase': 42.008}"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ens_mean = tf.reduce_mean(ensemble_preds, axis=0)\n",
    "ens_res = evaluate_predictions(y_true=test_labels, y_pred=ens_mean)\n",
    "ens_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plotting the 95% prediction interval (uncertainty estimates) of our ensemble\n",
    "\n",
    "One way to get the 95% confidence prediction intervals for a deep learninng model is the `bootstrap method`\n",
    "\n",
    "1- Take the predictions from a number of randomly initialized models\n",
    "\n",
    "2- Measure the standard deviation of the predictions\n",
    "\n",
    "3- Multiply the standard deviation by 1.96 (assuming the distribution is Gaussian/Normal, 95% of observations fall within 1.96 standard deviations of the mean)\n",
    "\n",
    "4- To get the prediction interval upper and lower bounds, add and substract the value obtained in (3) to the mean/median of the predictions made in (1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find upper and lower bounds of ensemble predictions\n",
    "def get_upper_lower(preds):\n",
    "    # 2- Measure the standard deviation of the predictions\n",
    "    std = tf.math.reduce_std(preds, axis=0)\n",
    "    # 3- Multiply the standard deviation by 1.96\n",
    "    interval = std * 1.96\n",
    "    # 4- Get the prediction interval upper and lower bounds\n",
    "    preds_mean = tf.reduce_mean(preds, axis=0)\n",
    "    lower, upper = preds_mean - interval, preds_mean + interval\n",
    "    return lower, upper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "lower, upper = get_upper_lower(ensemble_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x212dcd85270>"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "offset = 950\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.plot(test_labels.index[offset:], test_labels.values[offset:], 'o', label='test label')\n",
    "plt.plot(test_labels.index[offset:], ens_mean[offset:], '-', label='preds')\n",
    "\n",
    "plt.fill_between(test_labels.index[offset:], (lower)[offset:], (upper)[offset:], label='prediction interval')\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Types of uncertainty\n",
    "\n",
    "- Aleatoric uncertainty (data or subway uncertainty): it cannot be reduced with more data. No matter how much data we collect we cannot reduce this uncertainty.\n",
    "\n",
    "- Epistemic uncertainty (model or coconut uncertainty): which can be reduced with more data. Describe the confidence in the prediction"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make prediction into the future\n",
    "\n",
    "Train the model on the full historical data to make predictions into the future.\n",
    "\n",
    "NB: you need to retrain the model every time you want to generate a new prediction\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make features and labels\n",
    "x = bitcoin_prices_nbeats.dropna().drop('Price', axis=1)\n",
    "y = bitcoin_prices_nbeats.dropna()['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TIme to make our dataset performant using tf.data API\n",
    "features_dataset = tf.data.Dataset.from_tensor_slices(x)\n",
    "labels_dataset = tf.data.Dataset.from_tensor_slices(y)\n",
    "# combine labels and features by zipping together (features, labels)\n",
    "dataset = tf.data.Dataset.zip((features_dataset, labels_dataset))\n",
    "\n",
    "# Batching and prefech\n",
    "BATCH_SIZE = 1024\n",
    "dataset = dataset.batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set random seed for as reproductible results as possible\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model_6 = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dense(HORIZON)\n",
    "], name='model_6')\n",
    "\n",
    "model_6.compile(\n",
    "    loss='mae',\n",
    "    optimizer=tf.keras.optimizers.Adam(),\n",
    "    metrics=['mae', 'mse']\n",
    ")\n",
    "\n",
    "history_6 = model_6.fit(\n",
    "    dataset,\n",
    "    epochs=100,\n",
    "    verbose=0\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make predictions into future\n",
    "\n",
    "To make predictions into the future we want a function which:\n",
    "\n",
    "1- Take as input:\n",
    "    - a list of values (Bitcoin historical data)\n",
    "    - a trained model\n",
    "    - a window into the future to predict (`our INTO_FUTURE var`)\n",
    "    - the window size a model was trained on\n",
    "\n",
    "2- create an empty list for future forecasts. this will be returned at the end when it's full of predictions and extract the last window_size(here 7) values from the inputs values\n",
    "\n",
    "3- Loop `INTO_FUTURE` times making a prediction on `window_size` sequences which update to remove the first value and append the latest prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "INTO_FUTURE = 14\n",
    "def make_future_prediction(values, model, into_future=INTO_FUTURE, window_size=WINDOW_SISE) -> list:\n",
    "    \"\"\"\n",
    "    Make future forecasts into_fture steps after values ends\n",
    "    Return:\n",
    "        future firecasts as a list of float\n",
    "    \"\"\"\n",
    "    # create an empty list for future forecasts\n",
    "    future_forecast = []\n",
    "    last_window = values[-window_size:]\n",
    "    # Make into_future number of predictions, altering the data which gets predicted on each\n",
    "    for _ in range(into_future):\n",
    "        # predict on the last window then append it again, again, again\n",
    "        future_pred = model.predict(tf.expand_dims(last_window, axis=0))\n",
    "        print(f'Predicting on: \\n {last_window} -> Prediction: {tf.squeeze(future_pred).numpy()} \\n')\n",
    "        \n",
    "        # Append predictions\n",
    "        future_forecast.append(tf.squeeze(future_pred).numpy()) \n",
    "        # Upadate last window with new pred and get WINDOW_SIZE most recent\n",
    "        last_window = np.append(last_window, future_pred)[-window_size:]\n",
    "    return future_forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 24ms/step\n",
      "Predicting on: \n",
      " [28321.7 28723.  29674.5 29912.9 29984.2 33041.8 34562.9] -> Prediction: 28932.146484375 \n",
      "\n",
      "1/1 [==============================] - 0s 31ms/step\n",
      "Predicting on: \n",
      " [28723.         29674.5        29912.9        29984.2\n",
      " 33041.8        34562.9        28932.14648438] -> Prediction: 29653.29296875 \n",
      "\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "Predicting on: \n",
      " [29674.5        29912.9        29984.2        33041.8\n",
      " 34562.9        28932.14648438 29653.29296875] -> Prediction: 30442.962890625 \n",
      "\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "Predicting on: \n",
      " [29912.9        29984.2        33041.8        34562.9\n",
      " 28932.14648438 29653.29296875 30442.96289062] -> Prediction: 30597.560546875 \n",
      "\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "Predicting on: \n",
      " [29984.2        33041.8        34562.9        28932.14648438\n",
      " 29653.29296875 30442.96289062 30597.56054688] -> Prediction: 31566.783203125 \n",
      "\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "Predicting on: \n",
      " [33041.8        34562.9        28932.14648438 29653.29296875\n",
      " 30442.96289062 30597.56054688 31566.78320312] -> Prediction: 32936.98046875 \n",
      "\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "Predicting on: \n",
      " [34562.9        28932.14648438 29653.29296875 30442.96289062\n",
      " 30597.56054688 31566.78320312 32936.98046875] -> Prediction: 32914.6953125 \n",
      "\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "Predicting on: \n",
      " [28932.14648438 29653.29296875 30442.96289062 30597.56054688\n",
      " 31566.78320312 32936.98046875 32914.6953125 ] -> Prediction: 29730.685546875 \n",
      "\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "Predicting on: \n",
      " [29653.29296875 30442.96289062 30597.56054688 31566.78320312\n",
      " 32936.98046875 32914.6953125  29730.68554688] -> Prediction: 30466.33984375 \n",
      "\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "Predicting on: \n",
      " [30442.96289062 30597.56054688 31566.78320312 32936.98046875\n",
      " 32914.6953125  29730.68554688 30466.33984375] -> Prediction: 31146.32421875 \n",
      "\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "Predicting on: \n",
      " [30597.56054688 31566.78320312 32936.98046875 32914.6953125\n",
      " 29730.68554688 30466.33984375 31146.32421875] -> Prediction: 31374.34765625 \n",
      "\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "Predicting on: \n",
      " [31566.78320312 32936.98046875 32914.6953125  29730.68554688\n",
      " 30466.33984375 31146.32421875 31374.34765625] -> Prediction: 32319.19140625 \n",
      "\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "Predicting on: \n",
      " [32936.98046875 32914.6953125  29730.68554688 30466.33984375\n",
      " 31146.32421875 31374.34765625 32319.19140625] -> Prediction: 32714.857421875 \n",
      "\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "Predicting on: \n",
      " [32914.6953125  29730.68554688 30466.33984375 31146.32421875\n",
      " 31374.34765625 32319.19140625 32714.85742188] -> Prediction: 32210.09375 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "future_forecasts = make_future_prediction(y.values, model_6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_future_dates(start_date, into_future=INTO_FUTURE, offset=1):\n",
    "    \"\"\"\n",
    "    Return an array of datetime values ranging from start_date to start_date + into_future.\n",
    "\n",
    "    Parameters:\n",
    "    start_date (datetime): Starting date.\n",
    "    into_future (int): Number of days into the future, default is 7.\n",
    "    offset (int): Offset from the start_date, default is 1 day.\n",
    "\n",
    "    Returns:\n",
    "    numpy.ndarray: Array of dates from start_date + offset to start_date + into_future.\n",
    "    \"\"\"\n",
    "    # Ensure start_date is a numpy datetime64 object\n",
    "    start_date = np.datetime64(start_date)\n",
    "\n",
    "    # Calculate the start date with offset and end date\n",
    "    start_date_with_offset = start_date + np.timedelta64(offset, 'D')  # 'D' stands for day\n",
    "    end_date = start_date_with_offset + np.timedelta64(into_future, 'D')\n",
    "\n",
    "    # Generate and return the range of dates\n",
    "    return np.arange(start_date_with_offset, end_date, dtype='datetime64[D]')\n",
    "\n",
    "future_dates = get_future_dates(y.index[-1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "future_dates = np.insert(future_dates, 0, y.index[-1])\n",
    "future_forecasts = np.insert(future_forecasts, 0, y.values[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x212f3ae3e80>"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "offset = 4250\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.plot(y.index[offset:], y.values[offset:], '-', label='bitcoin data')\n",
    "plt.plot(future_dates, future_forecasts, '-', label='Future perdiction')\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>horizon=1, window_size=7, Dense, univariate</th>\n",
       "      <th>horizon=1, window_size=7, conv1D, univariate</th>\n",
       "      <th>horizon=1, window_size=7, Dense, multivariate</th>\n",
       "      <th>horizon=1, window_size=7, NBeats algo, univariate</th>\n",
       "      <th>horizon=1, window_size=7, Ensemble, univariate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mae</th>\n",
       "      <td>8.194720e+02</td>\n",
       "      <td>1.468349e+04</td>\n",
       "      <td>416.776337</td>\n",
       "      <td>8.265875e+02</td>\n",
       "      <td>8.280799e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mse</th>\n",
       "      <td>1.620772e+06</td>\n",
       "      <td>3.408884e+08</td>\n",
       "      <td>364025.000000</td>\n",
       "      <td>1.630284e+06</td>\n",
       "      <td>1.627460e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmse</th>\n",
       "      <td>1.273095e+03</td>\n",
       "      <td>1.788456e+04</td>\n",
       "      <td>603.344849</td>\n",
       "      <td>1.276826e+03</td>\n",
       "      <td>1.275719e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mape</th>\n",
       "      <td>2.283613e+00</td>\n",
       "      <td>4.756548e+01</td>\n",
       "      <td>1.165349</td>\n",
       "      <td>2.299408e+00</td>\n",
       "      <td>2.306975e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mase</th>\n",
       "      <td>4.200800e+01</td>\n",
       "      <td>4.200800e+01</td>\n",
       "      <td>42.007999</td>\n",
       "      <td>4.200800e+01</td>\n",
       "      <td>4.200800e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      horizon=1, window_size=7, Dense, univariate  \\\n",
       "mae                                  8.194720e+02   \n",
       "mse                                  1.620772e+06   \n",
       "rmse                                 1.273095e+03   \n",
       "mape                                 2.283613e+00   \n",
       "mase                                 4.200800e+01   \n",
       "\n",
       "      horizon=1, window_size=7, conv1D, univariate  \\\n",
       "mae                                   1.468349e+04   \n",
       "mse                                   3.408884e+08   \n",
       "rmse                                  1.788456e+04   \n",
       "mape                                  4.756548e+01   \n",
       "mase                                  4.200800e+01   \n",
       "\n",
       "      horizon=1, window_size=7, Dense, multivariate  \\\n",
       "mae                                      416.776337   \n",
       "mse                                   364025.000000   \n",
       "rmse                                     603.344849   \n",
       "mape                                       1.165349   \n",
       "mase                                      42.007999   \n",
       "\n",
       "      horizon=1, window_size=7, NBeats algo, univariate  \\\n",
       "mae                                        8.265875e+02   \n",
       "mse                                        1.630284e+06   \n",
       "rmse                                       1.276826e+03   \n",
       "mape                                       2.299408e+00   \n",
       "mase                                       4.200800e+01   \n",
       "\n",
       "      horizon=1, window_size=7, Ensemble, univariate  \n",
       "mae                                     8.280799e+02  \n",
       "mse                                     1.627460e+06  \n",
       "rmse                                    1.275719e+03  \n",
       "mape                                    2.306975e+00  \n",
       "mase                                    4.200800e+01  "
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# results = pd.DataFrame(pd.Series(np.expand_dims(model_1_results.values(), axis=0), index=['horizon=1, window_size=7, Dense, univariate']), columns=model_1_results.keys())\n",
    "results = pd.DataFrame.from_dict(model_1_results, orient='index', columns=['horizon=1, window_size=7, Dense, univariate'])\n",
    "results_1 = pd.DataFrame.from_dict(model_2_res, orient='index', columns=['horizon=1, window_size=7, conv1D, univariate'])\n",
    "results_2 = pd.DataFrame.from_dict(model_3_res, orient='index', columns=['horizon=1, window_size=7, Dense, multivariate'])\n",
    "results_3 = pd.DataFrame.from_dict(nbeats_res, orient='index', columns=['horizon=1, window_size=7, NBeats algo, univariate'])\n",
    "results_4 = pd.DataFrame.from_dict(ens_res, orient='index', columns=['horizon=1, window_size=7, Ensemble, univariate'])\n",
    "results = pd.concat([results, results_1, results_2, results_3, results_4], axis=1)\n",
    "results"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}