diff --git a/Brazil stocks/Brazil stocks prediction/.ipynb_checkpoints/Brazil prediction-checkpoint.ipynb b/Brazil stocks/Brazil stocks prediction/.ipynb_checkpoints/Brazil prediction-checkpoint.ipynb new file mode 100644 index 00000000..8c22330f --- /dev/null +++ b/Brazil stocks/Brazil stocks prediction/.ipynb_checkpoints/Brazil prediction-checkpoint.ipynb @@ -0,0 +1,16294 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "15854d64", + "metadata": { + "papermill": { + "duration": 0.010846, + "end_time": "2022-12-16T20:25:34.770881", + "exception": false, + "start_time": "2022-12-16T20:25:34.760035", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# IBOV EDA and Prediction with AR Model" + ] + }, + { + "cell_type": "markdown", + "id": "0a8866be", + "metadata": { + "papermill": { + "duration": 0.011403, + "end_time": "2022-12-16T20:25:34.810932", + "exception": false, + "start_time": "2022-12-16T20:25:34.799529", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Importing Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "bb3b8ec9", + "metadata": { + "execution": { + "iopub.execute_input": "2022-12-16T20:25:34.833224Z", + "iopub.status.busy": "2022-12-16T20:25:34.832223Z", + "iopub.status.idle": "2022-12-16T20:25:37.580635Z", + "shell.execute_reply": "2022-12-16T20:25:37.579608Z" + }, + "papermill": { + "duration": 2.76269, + "end_time": "2022-12-16T20:25:37.583355", + "exception": false, + "start_time": "2022-12-16T20:25:34.820665", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.metrics import mean_absolute_error\n", + "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n", + "from statsmodels.tsa.ar_model import AutoReg\n", + "import time\n", + "import seaborn as sns\n", + "import plotly.express as px\n", + "import numpy as np\n", + "import warnings\n" + ] + }, + { + "cell_type": "markdown", + "id": "96d29298", + "metadata": { + "papermill": { + "duration": 0.009387, + "end_time": "2022-12-16T20:25:37.602785", + "exception": false, + "start_time": "2022-12-16T20:25:37.593398", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Loading Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7a58fdfb", + "metadata": { + "execution": { + "iopub.execute_input": "2022-12-16T20:25:37.624061Z", + "iopub.status.busy": "2022-12-16T20:25:37.623308Z", + "iopub.status.idle": "2022-12-16T20:25:37.671526Z", + "shell.execute_reply": "2022-12-16T20:25:37.670557Z" + }, + "papermill": { + "duration": 0.062046, + "end_time": "2022-12-16T20:25:37.674363", + "exception": false, + "start_time": "2022-12-16T20:25:37.612317", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "df = pd.read_csv(r\"bovespa_indexes.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9cefa897", + "metadata": { + "execution": { + "iopub.execute_input": "2022-12-16T20:25:37.698475Z", + "iopub.status.busy": "2022-12-16T20:25:37.697662Z", + "iopub.status.idle": "2022-12-16T20:25:37.725550Z", + "shell.execute_reply": "2022-12-16T20:25:37.724337Z" + }, + "papermill": { + "duration": 0.044149, + "end_time": "2022-12-16T20:25:37.729206", + "exception": false, + "start_time": "2022-12-16T20:25:37.685057", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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02010-01-04^BVSP70045.070045.070081.068587.068587.01655400.0
12010-01-05^BVSP70240.070240.070595.069928.070046.01984200.0
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32010-01-07^BVSP70451.070451.070723.070045.070723.01555000.0
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+ "IMOB 1\n", + "IMAT 1\n", + "ICON 1\n", + "INDX 1\n", + "^IGCX 1\n", + "IFNC 1\n", + "^IEE 1\n", + "IGNM 1\n", + "Name: Symbol, dtype: int64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"Symbol\"].value_counts()" + ] + }, + { + "cell_type": "markdown", + "id": "64c1a79d", + "metadata": { + "papermill": { + "duration": 0.009835, + "end_time": "2022-12-16T20:25:37.868406", + "exception": false, + "start_time": "2022-12-16T20:25:37.858571", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Cleaning Data" + ] + }, + { + "cell_type": "markdown", + "id": "3bebee89", + "metadata": { + "papermill": { + "duration": 0.00967, + "end_time": "2022-12-16T20:25:37.888364", + "exception": false, + "start_time": "2022-12-16T20:25:37.878694", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Converting the date column to datetime:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(15, 6))\n", + "plot_pacf(y, ax=ax)\n", + "plt.xlabel(\"Lag [Days]\")\n", + "plt.ylabel(\"Correlation Coefficient\");" + ] + }, + { + "cell_type": "markdown", + "id": "57fd56e3", + "metadata": { + "papermill": { + "duration": 0.013354, + "end_time": "2022-12-16T20:25:40.044146", + "exception": false, + "start_time": "2022-12-16T20:25:40.030792", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The graph above shows that there is a possible good predictive power with 19 lags." + ] + }, + { + "cell_type": "markdown", + "id": "373ca591", + "metadata": { + "papermill": { + "duration": 0.012667, + "end_time": "2022-12-16T20:25:40.069760", + "exception": false, + "start_time": "2022-12-16T20:25:40.057093", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Model Building" + ] + }, + { + "cell_type": "markdown", + "id": "348f8289", + "metadata": { + "papermill": { + "duration": 0.012528, + "end_time": "2022-12-16T20:25:40.095300", + "exception": false, + "start_time": "2022-12-16T20:25:40.082772", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Spliting the Data" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "e2b0e70a", + "metadata": { + "execution": { + "iopub.execute_input": "2022-12-16T20:25:40.122809Z", + "iopub.status.busy": "2022-12-16T20:25:40.122388Z", + "iopub.status.idle": "2022-12-16T20:25:40.129052Z", + "shell.execute_reply": "2022-12-16T20:25:40.127504Z" + }, + "papermill": { + "duration": 0.023322, + "end_time": "2022-12-16T20:25:40.131400", + "exception": false, + "start_time": "2022-12-16T20:25:40.108078", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "cutoff_test = int(len(y)*0.90)\n", + "\n", + "y_train = y.iloc[:cutoff_test]\n", + "y_test = y.iloc[cutoff_test:]" + ] + }, + { + "cell_type": "markdown", + "id": "0a204c82", + "metadata": { + "papermill": { + "duration": 0.013375, + "end_time": "2022-12-16T20:25:40.158515", + "exception": false, + "start_time": "2022-12-16T20:25:40.145140", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Setting 90% for train and 10 % for test." + ] + }, + { + "cell_type": "markdown", + "id": "f50cc5ce", + "metadata": { + "papermill": { + "duration": 0.013003, + "end_time": "2022-12-16T20:25:40.185454", + "exception": false, + "start_time": "2022-12-16T20:25:40.172451", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Defining Baseline" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "909df3a0", + "metadata": { + "execution": { + "iopub.execute_input": "2022-12-16T20:25:40.213434Z", + "iopub.status.busy": "2022-12-16T20:25:40.213012Z", + "iopub.status.idle": "2022-12-16T20:25:40.222570Z", + "shell.execute_reply": "2022-12-16T20:25:40.221313Z" + }, + "papermill": { + "duration": 0.026455, + "end_time": "2022-12-16T20:25:40.225095", + "exception": false, + "start_time": "2022-12-16T20:25:40.198640", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of the IBOV: 75202.64\n", + "Baseline MAE: 20841.62\n" + ] + } + ], + "source": [ + "y_train_mean = y_train.mean()\n", + "y_pred_baseline = [y_train_mean] * len(y_train)\n", + "mae_baseline = mean_absolute_error(y_train, y_pred_baseline)\n", + "\n", + "print(\"Mean of the IBOV:\", round(y_train_mean, 2))\n", + "print(\"Baseline MAE:\", round(mae_baseline, 2))" + ] + }, + { + "cell_type": "markdown", + "id": "9c393217", + "metadata": { + "papermill": { + "duration": 0.013839, + "end_time": "2022-12-16T20:25:40.253253", + "exception": false, + "start_time": "2022-12-16T20:25:40.239414", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# AutoReg Model" + ] + }, + { + "cell_type": "markdown", + "id": "a817f9d2", + "metadata": { + "papermill": { + "duration": 0.014085, + "end_time": "2022-12-16T20:25:40.281704", + "exception": false, + "start_time": "2022-12-16T20:25:40.267619", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Creating the pipeline:" + ] + }, + { + "cell_type": "markdown", + "id": "1c98c0d9", + "metadata": { + "papermill": { + "duration": 0.013587, + "end_time": "2022-12-16T20:25:40.309441", + "exception": false, + "start_time": "2022-12-16T20:25:40.295854", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Testing Lags for AutoReg model:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "561a36b1", + "metadata": { + "execution": { + "iopub.execute_input": "2022-12-16T20:25:40.338271Z", + "iopub.status.busy": "2022-12-16T20:25:40.337625Z", + "iopub.status.idle": "2022-12-16T20:25:40.649538Z", + "shell.execute_reply": "2022-12-16T20:25:40.647944Z" + }, + "papermill": { + "duration": 0.330629, + "end_time": "2022-12-16T20:25:40.653583", + "exception": false, + "start_time": "2022-12-16T20:25:40.322954", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trained AutoReg 1 in 0.01 seconds.\n", + "Trained AutoReg 2 in 0.0 seconds.\n", + "Trained AutoReg 3 in 0.0 seconds.\n", + "Trained AutoReg 4 in 0.0 seconds.\n", + "Trained AutoReg 5 in 0.0 seconds.\n", + "Trained AutoReg 6 in 0.0 seconds.\n", + "Trained AutoReg 7 in 0.01 seconds.\n", + "Trained AutoReg 8 in 0.0 seconds.\n", + "Trained AutoReg 9 in 0.02 seconds.\n", + "Trained AutoReg 10 in 0.01 seconds.\n", + "Trained AutoReg 11 in 0.01 seconds.\n", + "Trained AutoReg 12 in 0.02 seconds.\n", + "Trained AutoReg 13 in 0.03 seconds.\n", + "Trained AutoReg 14 in 0.01 seconds.\n", + "Trained AutoReg 15 in 0.01 seconds.\n", + "Trained AutoReg 16 in 0.01 seconds.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trained AutoReg 17 in 0.03 seconds.\n", + "Trained AutoReg 18 in 0.02 seconds.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n" + ] + } + ], + "source": [ + "# Create dictionary to store MAEs\n", + "mae_grid = dict()\n", + "p_params = range(1,19)\n", + "# Outer loop: Iterate through possible values for p\n", + "for p in p_params:\n", + " mae_grid[p] = list()\n", + " # Note start time\n", + " start_time = time.time()\n", + " # Train model\n", + " model = AutoReg(y_train, lags=p).fit()\n", + " # Calculate model training time\n", + " elapsed_time = round(time.time() - start_time, 2)\n", + " print(f\"Trained AutoReg {p} in {elapsed_time} seconds.\")\n", + " # Generate in-sample (training) predictions\n", + " y_pred = model.predict().dropna()\n", + " # Calculate training MAE\n", + " mae = mean_absolute_error(y_train[p:], y_pred)\n", + " # Append MAE to list in dictionary\n", + " mae_grid[p].append(mae)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "437fa117", + "metadata": { + 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0564.426600569.893900570.017700570.234200572.023900572.328900572.471100573.201700574.584200575.797700576.596900582.906500584.887800585.001400584.966900585.752800585.743700585.578100
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mae_df = pd.DataFrame(mae_grid)\n", + "mae_df.round(4).style.background_gradient(axis=None)" + ] + }, + { + "cell_type": "markdown", + "id": "a60c47f2", + "metadata": { + "papermill": { + "duration": 0.01425, + "end_time": "2022-12-16T20:25:40.866441", + "exception": false, + "start_time": "2022-12-16T20:25:40.852191", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As the heatmap above shows, the best lag is 1." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "59f58566", + "metadata": { + "execution": { + "iopub.execute_input": "2022-12-16T20:25:40.897490Z", + "iopub.status.busy": "2022-12-16T20:25:40.896629Z", + "iopub.status.idle": "2022-12-16T20:25:40.907355Z", + "shell.execute_reply": "2022-12-16T20:25:40.906294Z" + }, + "papermill": { + "duration": 0.029109, + "end_time": "2022-12-16T20:25:40.910024", + "exception": false, + "start_time": "2022-12-16T20:25:40.880915", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n" + ] + } + ], + "source": [ + "model = AutoReg(y_train, lags=1).fit()" + ] + }, + { + "cell_type": "markdown", + "id": "30c05fca", + "metadata": { + "papermill": { + "duration": 0.014277, + "end_time": "2022-12-16T20:25:40.938936", + "exception": false, + "start_time": "2022-12-16T20:25:40.924659", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Setting parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "9c47d815", + "metadata": { + "execution": { + "iopub.execute_input": "2022-12-16T20:25:40.970343Z", + "iopub.status.busy": "2022-12-16T20:25:40.969316Z", + "iopub.status.idle": "2022-12-16T20:25:40.979146Z", + "shell.execute_reply": "2022-12-16T20:25:40.978059Z" + }, + "papermill": { + "duration": 0.027528, + "end_time": "2022-12-16T20:25:40.981402", + "exception": false, + "start_time": "2022-12-16T20:25:40.953874", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training MAE: 564.4266272852714\n" + ] + } + ], + "source": [ + "y_pred = model.predict().dropna()\n", + "training_mae = mean_absolute_error(y_train[1:], y_pred)\n", + "print(\"Training MAE:\", training_mae)" + ] + }, + { + "cell_type": "markdown", + "id": "2d3ce461", + "metadata": { + "papermill": { + "duration": 0.013581, + "end_time": "2022-12-16T20:25:41.009195", + "exception": false, + "start_time": "2022-12-16T20:25:40.995614", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Walk foward validation:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "68882ea1", + "metadata": { + "execution": { + "iopub.execute_input": "2022-12-16T20:25:41.039923Z", + "iopub.status.busy": "2022-12-16T20:25:41.039360Z", + "iopub.status.idle": "2022-12-16T20:25:46.503349Z", + "shell.execute_reply": "2022-12-16T20:25:46.502230Z" + }, + "papermill": { + "duration": 5.483351, + "end_time": "2022-12-16T20:25:46.506835", + "exception": false, + "start_time": "2022-12-16T20:25:41.023484", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "%%capture\n", + "\n", + "y_pred_wfv = pd.Series()\n", + "history = y_train.copy()\n", + "####\n", + "for i in range(len(y_test)):\n", + " model = AutoReg(history, lags=1).fit()\n", + " next_pred = model.forecast()\n", + " y_pred_wfv = y_pred_wfv.append(next_pred)\n", + " history = history.append(y_test[next_pred.index])" + ] + }, + { + "cell_type": "markdown", + "id": "f470729b", + "metadata": { + "papermill": { + "duration": 0.020998, + "end_time": "2022-12-16T20:25:46.549751", + "exception": false, + "start_time": "2022-12-16T20:25:46.528753", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Evaluating the model:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "baae2201", + "metadata": { + "execution": { + "iopub.execute_input": "2022-12-16T20:25:46.594355Z", + "iopub.status.busy": "2022-12-16T20:25:46.593854Z", + "iopub.status.idle": "2022-12-16T20:25:46.601316Z", + "shell.execute_reply": "2022-12-16T20:25:46.600266Z" + }, + "papermill": { + "duration": 0.037132, + "end_time": "2022-12-16T20:25:46.608442", + "exception": false, + "start_time": "2022-12-16T20:25:46.571310", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test MAE (walk forward validation): 590.15\n" + ] + } + ], + "source": [ + "test_mae = mean_absolute_error(y_test, y_pred_wfv)\n", + "print(\"Test MAE (walk forward validation):\", round(test_mae, 2))" + ] + }, + { + "cell_type": "markdown", + "id": "e2601a6c", + "metadata": { + "papermill": { + "duration": 0.013829, + "end_time": "2022-12-16T20:25:46.643754", + "exception": false, + "start_time": "2022-12-16T20:25:46.629925", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Prediction vs Real Graph" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "35e6641f", + "metadata": { + "execution": { + "iopub.execute_input": "2022-12-16T20:25:46.673068Z", + "iopub.status.busy": "2022-12-16T20:25:46.672561Z", + "iopub.status.idle": "2022-12-16T20:25:46.787036Z", + "shell.execute_reply": "2022-12-16T20:25:46.785780Z" + }, + "papermill": { + "duration": 0.132233, + "end_time": "2022-12-16T20:25:46.789764", + "exception": false, + "start_time": "2022-12-16T20:25:46.657531", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_pred_test = pd.DataFrame(\n", + " {\"y_test\": y_test, \"y_pred_wfv\": y_pred_wfv}\n", + ")\n", + "fig = px.line(df_pred_test, labels={\"value\":\"IBOV\"}, title= \"Brazil Index prediction\")\n", + "fig.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.5" + }, + "papermill": { + "default_parameters": {}, + "duration": 21.733667, + "end_time": "2022-12-16T20:25:47.783998", + "environment_variables": {}, + "exception": null, + "input_path": "__notebook__.ipynb", + "output_path": "__notebook__.ipynb", + "parameters": {}, + "start_time": "2022-12-16T20:25:26.050331", + "version": "2.3.4" + }, + "toc-autonumbering": false, + "toc-showmarkdowntxt": false + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Brazil stocks/Brazil stocks prediction/Brazil prediction.ipynb b/Brazil stocks/Brazil stocks prediction/Brazil prediction.ipynb new file mode 100644 index 00000000..03e5f52b --- /dev/null +++ b/Brazil stocks/Brazil stocks prediction/Brazil prediction.ipynb @@ -0,0 +1,16277 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0a8866be", + "metadata": { + "papermill": { + "duration": 0.011403, + "end_time": "2022-12-16T20:25:34.810932", + "exception": false, + "start_time": "2022-12-16T20:25:34.799529", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Importing Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "bb3b8ec9", + "metadata": { + "execution": { + "iopub.execute_input": "2022-12-16T20:25:34.833224Z", + "iopub.status.busy": "2022-12-16T20:25:34.832223Z", + "iopub.status.idle": "2022-12-16T20:25:37.580635Z", + "shell.execute_reply": "2022-12-16T20:25:37.579608Z" + }, + "papermill": { + "duration": 2.76269, + "end_time": "2022-12-16T20:25:37.583355", + "exception": false, + "start_time": "2022-12-16T20:25:34.820665", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.metrics import mean_absolute_error\n", + "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n", + "from statsmodels.tsa.ar_model import AutoReg\n", + "import time\n", + "import seaborn as sns\n", + "import plotly.express as px\n", + "import numpy as np\n", + "import warnings\n" + ] + }, + { + "cell_type": "markdown", + "id": "96d29298", + "metadata": { + "papermill": { + "duration": 0.009387, + "end_time": "2022-12-16T20:25:37.602785", + "exception": false, + "start_time": "2022-12-16T20:25:37.593398", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Loading Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7a58fdfb", + "metadata": { + "execution": { + "iopub.execute_input": "2022-12-16T20:25:37.624061Z", + "iopub.status.busy": "2022-12-16T20:25:37.623308Z", + "iopub.status.idle": "2022-12-16T20:25:37.671526Z", + "shell.execute_reply": "2022-12-16T20:25:37.670557Z" + }, + "papermill": { + "duration": 0.062046, + "end_time": "2022-12-16T20:25:37.674363", + "exception": false, + "start_time": "2022-12-16T20:25:37.612317", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "df = pd.read_csv(r\"bovespa_indexes.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9cefa897", + "metadata": { + "execution": { + "iopub.execute_input": "2022-12-16T20:25:37.698475Z", + "iopub.status.busy": "2022-12-16T20:25:37.697662Z", + "iopub.status.idle": "2022-12-16T20:25:37.725550Z", + "shell.execute_reply": "2022-12-16T20:25:37.724337Z" + }, + "papermill": { + "duration": 0.044149, + "end_time": "2022-12-16T20:25:37.729206", + "exception": false, + "start_time": "2022-12-16T20:25:37.685057", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(15, 6))\n", + "plot_pacf(y, ax=ax)\n", + "plt.xlabel(\"Lag [Days]\")\n", + "plt.ylabel(\"Correlation Coefficient\");" + ] + }, + { + "cell_type": "markdown", + "id": "57fd56e3", + "metadata": { + "papermill": { + "duration": 0.013354, + "end_time": "2022-12-16T20:25:40.044146", + "exception": false, + "start_time": "2022-12-16T20:25:40.030792", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The graph above shows that there is a possible good predictive power with 19 lags." + ] + }, + { + "cell_type": "markdown", + "id": "373ca591", + "metadata": { + "papermill": { + "duration": 0.012667, + "end_time": "2022-12-16T20:25:40.069760", + "exception": false, + "start_time": "2022-12-16T20:25:40.057093", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Model Building" + ] + }, + { + "cell_type": "markdown", + "id": "348f8289", + "metadata": { + "papermill": { + "duration": 0.012528, + "end_time": "2022-12-16T20:25:40.095300", + "exception": false, + "start_time": "2022-12-16T20:25:40.082772", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Spliting the Data" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "e2b0e70a", + "metadata": { + "execution": { + "iopub.execute_input": "2022-12-16T20:25:40.122809Z", + "iopub.status.busy": "2022-12-16T20:25:40.122388Z", + "iopub.status.idle": "2022-12-16T20:25:40.129052Z", + "shell.execute_reply": "2022-12-16T20:25:40.127504Z" + }, + "papermill": { + "duration": 0.023322, + "end_time": "2022-12-16T20:25:40.131400", + "exception": false, + "start_time": "2022-12-16T20:25:40.108078", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "cutoff_test = int(len(y)*0.90)\n", + "\n", + "y_train = y.iloc[:cutoff_test]\n", + "y_test = y.iloc[cutoff_test:]" + ] + }, + { + "cell_type": "markdown", + "id": "0a204c82", + "metadata": { + "papermill": { + "duration": 0.013375, + "end_time": "2022-12-16T20:25:40.158515", + "exception": false, + "start_time": "2022-12-16T20:25:40.145140", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Setting 90% for train and 10 % for test." + ] + }, + { + "cell_type": "markdown", + "id": "f50cc5ce", + "metadata": { + "papermill": { + "duration": 0.013003, + "end_time": "2022-12-16T20:25:40.185454", + "exception": false, + "start_time": "2022-12-16T20:25:40.172451", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Defining Baseline" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "909df3a0", + "metadata": { + "execution": { + "iopub.execute_input": "2022-12-16T20:25:40.213434Z", + "iopub.status.busy": "2022-12-16T20:25:40.213012Z", + "iopub.status.idle": "2022-12-16T20:25:40.222570Z", + "shell.execute_reply": "2022-12-16T20:25:40.221313Z" + }, + "papermill": { + "duration": 0.026455, + "end_time": "2022-12-16T20:25:40.225095", + "exception": false, + "start_time": "2022-12-16T20:25:40.198640", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of the IBOV: 75202.64\n", + "Baseline MAE: 20841.62\n" + ] + } + ], + "source": [ + "y_train_mean = y_train.mean()\n", + "y_pred_baseline = [y_train_mean] * len(y_train)\n", + "mae_baseline = mean_absolute_error(y_train, y_pred_baseline)\n", + "\n", + "print(\"Mean of the IBOV:\", round(y_train_mean, 2))\n", + "print(\"Baseline MAE:\", round(mae_baseline, 2))" + ] + }, + { + "cell_type": "markdown", + "id": "9c393217", + "metadata": { + "papermill": { + "duration": 0.013839, + "end_time": "2022-12-16T20:25:40.253253", + "exception": false, + "start_time": "2022-12-16T20:25:40.239414", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# AutoReg Model" + ] + }, + { + "cell_type": "markdown", + "id": "a817f9d2", + "metadata": { + "papermill": { + "duration": 0.014085, + "end_time": "2022-12-16T20:25:40.281704", + "exception": false, + "start_time": "2022-12-16T20:25:40.267619", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Creating the pipeline:" + ] + }, + { + "cell_type": "markdown", + "id": "1c98c0d9", + "metadata": { + "papermill": { + "duration": 0.013587, + "end_time": "2022-12-16T20:25:40.309441", + "exception": false, + "start_time": "2022-12-16T20:25:40.295854", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Testing Lags for AutoReg model:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "561a36b1", + "metadata": { + "execution": { + "iopub.execute_input": "2022-12-16T20:25:40.338271Z", + "iopub.status.busy": "2022-12-16T20:25:40.337625Z", + "iopub.status.idle": "2022-12-16T20:25:40.649538Z", + "shell.execute_reply": "2022-12-16T20:25:40.647944Z" + }, + "papermill": { + "duration": 0.330629, + "end_time": "2022-12-16T20:25:40.653583", + "exception": false, + "start_time": "2022-12-16T20:25:40.322954", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trained AutoReg 1 in 0.01 seconds.\n", + "Trained AutoReg 2 in 0.0 seconds.\n", + "Trained AutoReg 3 in 0.0 seconds.\n", + "Trained AutoReg 4 in 0.0 seconds.\n", + "Trained AutoReg 5 in 0.0 seconds.\n", + "Trained AutoReg 6 in 0.0 seconds.\n", + "Trained AutoReg 7 in 0.01 seconds.\n", + "Trained AutoReg 8 in 0.0 seconds.\n", + "Trained AutoReg 9 in 0.02 seconds.\n", + "Trained AutoReg 10 in 0.01 seconds.\n", + "Trained AutoReg 11 in 0.01 seconds.\n", + "Trained AutoReg 12 in 0.02 seconds.\n", + "Trained AutoReg 13 in 0.03 seconds.\n", + "Trained AutoReg 14 in 0.01 seconds.\n", + "Trained AutoReg 15 in 0.01 seconds.\n", + "Trained AutoReg 16 in 0.01 seconds.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trained AutoReg 17 in 0.03 seconds.\n", + "Trained AutoReg 18 in 0.02 seconds.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n" + ] + } + ], + "source": [ + "# Create dictionary to store MAEs\n", + "mae_grid = dict()\n", + "p_params = range(1,19)\n", + "# Outer loop: Iterate through possible values for p\n", + "for p in p_params:\n", + " mae_grid[p] = list()\n", + " # Note start time\n", + " start_time = time.time()\n", + " # Train model\n", + " model = AutoReg(y_train, lags=p).fit()\n", + " # Calculate model training time\n", + " elapsed_time = round(time.time() - start_time, 2)\n", + " print(f\"Trained AutoReg {p} in {elapsed_time} seconds.\")\n", + " # Generate in-sample (training) predictions\n", + " y_pred = model.predict().dropna()\n", + " # Calculate training MAE\n", + " mae = mean_absolute_error(y_train[p:], y_pred)\n", + " # Append MAE to list in dictionary\n", + " mae_grid[p].append(mae)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "437fa117", + "metadata": { + 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0564.426600569.893900570.017700570.234200572.023900572.328900572.471100573.201700574.584200575.797700576.596900582.906500584.887800585.001400584.966900585.752800585.743700585.578100
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mae_df = pd.DataFrame(mae_grid)\n", + "mae_df.round(4).style.background_gradient(axis=None)" + ] + }, + { + "cell_type": "markdown", + "id": "a60c47f2", + "metadata": { + "papermill": { + "duration": 0.01425, + "end_time": "2022-12-16T20:25:40.866441", + "exception": false, + "start_time": "2022-12-16T20:25:40.852191", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As the heatmap above shows, the best lag is 1." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "59f58566", + "metadata": { + "execution": { + "iopub.execute_input": "2022-12-16T20:25:40.897490Z", + "iopub.status.busy": "2022-12-16T20:25:40.896629Z", + "iopub.status.idle": "2022-12-16T20:25:40.907355Z", + "shell.execute_reply": "2022-12-16T20:25:40.906294Z" + }, + "papermill": { + "duration": 0.029109, + "end_time": "2022-12-16T20:25:40.910024", + "exception": false, + "start_time": "2022-12-16T20:25:40.880915", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency D will be used.\n", + "\n" + ] + } + ], + "source": [ + "model = AutoReg(y_train, lags=1).fit()" + ] + }, + { + "cell_type": "markdown", + "id": "30c05fca", + "metadata": { + "papermill": { + "duration": 0.014277, + "end_time": "2022-12-16T20:25:40.938936", + "exception": false, + "start_time": "2022-12-16T20:25:40.924659", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Setting parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "9c47d815", + "metadata": { + "execution": { + "iopub.execute_input": "2022-12-16T20:25:40.970343Z", + "iopub.status.busy": "2022-12-16T20:25:40.969316Z", + "iopub.status.idle": "2022-12-16T20:25:40.979146Z", + "shell.execute_reply": "2022-12-16T20:25:40.978059Z" + }, + "papermill": { + "duration": 0.027528, + "end_time": "2022-12-16T20:25:40.981402", + "exception": false, + "start_time": "2022-12-16T20:25:40.953874", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training MAE: 564.4266272852714\n" + ] + } + ], + "source": [ + "y_pred = model.predict().dropna()\n", + "training_mae = mean_absolute_error(y_train[1:], y_pred)\n", + "print(\"Training MAE:\", training_mae)" + ] + }, + { + "cell_type": "markdown", + "id": "2d3ce461", + "metadata": { + "papermill": { + "duration": 0.013581, + "end_time": "2022-12-16T20:25:41.009195", + "exception": false, + "start_time": "2022-12-16T20:25:40.995614", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Walk foward validation:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "68882ea1", + "metadata": { + "execution": { + "iopub.execute_input": "2022-12-16T20:25:41.039923Z", + "iopub.status.busy": "2022-12-16T20:25:41.039360Z", + "iopub.status.idle": "2022-12-16T20:25:46.503349Z", + "shell.execute_reply": "2022-12-16T20:25:46.502230Z" + }, + "papermill": { + "duration": 5.483351, + "end_time": "2022-12-16T20:25:46.506835", + "exception": false, + "start_time": "2022-12-16T20:25:41.023484", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "%%capture\n", + "\n", + "y_pred_wfv = pd.Series()\n", + "history = y_train.copy()\n", + "####\n", + "for i in range(len(y_test)):\n", + " model = AutoReg(history, lags=1).fit()\n", + " next_pred = model.forecast()\n", + " y_pred_wfv = y_pred_wfv.append(next_pred)\n", + " history = history.append(y_test[next_pred.index])" + ] + }, + { + "cell_type": "markdown", + "id": "f470729b", + "metadata": { + "papermill": { + "duration": 0.020998, + "end_time": "2022-12-16T20:25:46.549751", + "exception": false, + "start_time": "2022-12-16T20:25:46.528753", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Evaluating the model:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "baae2201", + "metadata": { + "execution": { + "iopub.execute_input": "2022-12-16T20:25:46.594355Z", + "iopub.status.busy": "2022-12-16T20:25:46.593854Z", + "iopub.status.idle": "2022-12-16T20:25:46.601316Z", + "shell.execute_reply": "2022-12-16T20:25:46.600266Z" + }, + "papermill": { + "duration": 0.037132, + "end_time": "2022-12-16T20:25:46.608442", + "exception": false, + "start_time": "2022-12-16T20:25:46.571310", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test MAE (walk forward validation): 590.15\n" + ] + } + ], + "source": [ + "test_mae = mean_absolute_error(y_test, y_pred_wfv)\n", + "print(\"Test MAE (walk forward validation):\", round(test_mae, 2))" + ] + }, + { + "cell_type": "markdown", + "id": "e2601a6c", + "metadata": { + "papermill": { + "duration": 0.013829, + "end_time": "2022-12-16T20:25:46.643754", + "exception": false, + "start_time": "2022-12-16T20:25:46.629925", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Prediction vs Real Graph" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "35e6641f", + "metadata": { + "execution": { + "iopub.execute_input": "2022-12-16T20:25:46.673068Z", + "iopub.status.busy": "2022-12-16T20:25:46.672561Z", + "iopub.status.idle": "2022-12-16T20:25:46.787036Z", + "shell.execute_reply": "2022-12-16T20:25:46.785780Z" + }, + "papermill": { + "duration": 0.132233, + "end_time": "2022-12-16T20:25:46.789764", + "exception": false, + "start_time": "2022-12-16T20:25:46.657531", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": 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