Autoregressive_Model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOuWyL64PsIc/7tr0PoZI09",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tusharbansal22/Tensorflow-Notebooks/blob/main/Autoregressive_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FDYmoRBrZwD6",
        "outputId": "7ad38c44-b43e-436b-e2e5-9f7b5cff5d47"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.9.2\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Input,Dense\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import SGD,Adam\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "Dn0np4HNZ53n"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "series= np.sin(0.1*np.arange(200 ))+ np.random.randn(200)*0.1\n",
        "plt.plot(series)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "cAXccLQtaMUK",
        "outputId": "da4d9d6d-e289-4a73-dcd3-2258173e3392"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "T=10\n",
        "X=[]\n",
        "Y=[]\n",
        "for t in range(0,len(series)-T):\n",
        "  x=series[t:t+T]\n",
        "  X.append(x)\n",
        "  y=series[t+T]\n",
        "  Y.append(y)\n",
        "X=np.array(X).reshape(-1,T)\n",
        "Y=np.array(Y)\n",
        "N=len(x)\n",
        "print(\"X.shape :\",X.shape,\"Y.shape :\",Y.shape)"
      ],
      "metadata": {
        "id": "W4YbqUnaapwi",
        "outputId": "dda017cf-736f-4406-ea3b-9a5f17927a7b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X.shape : (190, 10) Y.shape : (190,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "i=Input(shape=(T,))\n",
        "x=Dense(1)(i)\n",
        "model=Model(i,x)\n",
        "model.compile(loss='mse',optimizer=Adam(lr=0.1))\n",
        "r=model.fit(X[:N//2],Y[:N//2],epochs=80,validation_data=(X[-N//2:],Y[-N//2:]))"
      ],
      "metadata": {
        "id": "2BGcv8P2beNA",
        "outputId": "24b6ecac-fffe-4343-d2ab-e104b068be49",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/80\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 409ms/step - loss: 4.0905 - val_loss: 0.7384\n",
            "Epoch 2/80\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 1.8658 - val_loss: 0.2247\n",
            "Epoch 3/80\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 0.5406 - val_loss: 0.0195\n",
            "Epoch 4/80\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.0474 - val_loss: 0.0500\n",
            "Epoch 5/80\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.1835 - val_loss: 0.1904\n",
            "Epoch 6/80\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6021 - val_loss: 0.3130\n",
            "Epoch 7/80\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.9542 - val_loss: 0.3521\n",
            "Epoch 8/80\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 1.0636 - val_loss: 0.3085\n",
            "Epoch 9/80\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.9366 - val_loss: 0.2166\n",
            "Epoch 10/80\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.6704 - val_loss: 0.1170\n",
            "Epoch 11/80\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.3787 - val_loss: 0.0420\n",
            "Epoch 12/80\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.1521 - val_loss: 0.0095\n",
            "Epoch 13/80\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.0411 - val_loss: 0.0204\n",
            "Epoch 14/80\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.0504 - val_loss: 0.0608\n",
            "Epoch 15/80\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.1439 - val_loss: 0.1088\n",
            "Epoch 16/80\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.2632 - val_loss: 0.1435\n",
            "Epoch 17/80\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.3519 - val_loss: 0.1528\n",
            "Epoch 18/80\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.3768 - val_loss: 0.1359\n",
            "Epoch 19/80\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.3352 - val_loss: 0.1012\n",
            "Epoch 20/80\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.2484 - val_loss: 0.0615\n",
            "Epoch 21/80\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.1498 - val_loss: 0.0288\n",
            "Epoch 22/80\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.0711 - val_loss: 0.0108\n",
            "Epoch 23/80\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.0321 - val_loss: 0.0092\n",
            "Epoch 24/80\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.0361 - val_loss: 0.0196\n",
            "Epoch 25/80\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.0708 - val_loss: 0.0343\n",
            "Epoch 26/80\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.1149 - val_loss: 0.0455\n",
            "Epoch 27/80\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.1472 - val_loss: 0.0485\n",
            "Epoch 28/80\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.1548 - val_loss: 0.0426\n",
            "Epoch 29/80\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.1366 - val_loss: 0.0310\n",
            "Epoch 30/80\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.1015 - val_loss: 0.0186\n",
            "Epoch 31/80\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.0637 - val_loss: 0.0100\n",
            "Epoch 32/80\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.0363 - val_loss: 0.0078\n",
            "Epoch 33/80\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.0263 - val_loss: 0.0115\n",
            "Epoch 34/80\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.0331 - val_loss: 0.0184\n",
            "Epoch 35/80\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.0497 - val_loss: 0.0249\n",
            "Epoch 36/80\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.0662 - val_loss: 0.0281\n",
            "Epoch 37/80\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.0746 - val_loss: 0.0268\n",
            "Epoch 38/80\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.0716 - val_loss: 0.0219\n",
            "Epoch 39/80\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.0592 - val_loss: 0.0155\n",
            "Epoch 40/80\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.0432 - val_loss: 0.0102\n",
            "Epoch 41/80\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.0301 - val_loss: 0.0075\n",
            "Epoch 42/80\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.0240 - val_loss: 0.0078\n",
            "Epoch 43/80\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.0257 - val_loss: 0.0101\n",
            "Epoch 44/80\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.0324 - val_loss: 0.0126\n",
            "Epoch 45/80\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.0394 - val_loss: 0.0140\n",
            "Epoch 46/80\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.0430 - val_loss: 0.0136\n",
            "Epoch 47/80\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.0415 - val_loss: 0.0117\n",
            "Epoch 48/80\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.0359 - val_loss: 0.0093\n",
            "Epoch 49/80\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0289 - val_loss: 0.0075\n",
            "Epoch 50/80\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.0236 - val_loss: 0.0070\n",
            "Epoch 51/80\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.0216 - val_loss: 0.0076\n",
            "Epoch 52/80\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.0230 - val_loss: 0.0087\n",
            "Epoch 53/80\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.0260 - val_loss: 0.0097\n",
            "Epoch 54/80\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.0286 - val_loss: 0.0100\n",
            "Epoch 55/80\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.0292 - val_loss: 0.0094\n",
            "Epoch 56/80\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.0275 - val_loss: 0.0083\n",
            "Epoch 57/80\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.0245 - val_loss: 0.0072\n",
            "Epoch 58/80\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.0215 - val_loss: 0.0067\n",
            "Epoch 59/80\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.0198 - val_loss: 0.0068\n",
            "Epoch 60/80\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.0198 - val_loss: 0.0072\n",
            "Epoch 61/80\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.0209 - val_loss: 0.0078\n",
            "Epoch 62/80\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.0221 - val_loss: 0.0080\n",
            "Epoch 63/80\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.0225 - val_loss: 0.0079\n",
            "Epoch 64/80\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.0219 - val_loss: 0.0074\n",
            "Epoch 65/80\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.0205 - val_loss: 0.0068\n",
            "Epoch 66/80\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.0190 - val_loss: 0.0064\n",
            "Epoch 67/80\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.0181 - val_loss: 0.0064\n",
            "Epoch 68/80\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.0178 - val_loss: 0.0065\n",
            "Epoch 69/80\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.0182 - val_loss: 0.0066\n",
            "Epoch 70/80\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.0187 - val_loss: 0.0067\n",
            "Epoch 71/80\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.0188 - val_loss: 0.0066\n",
            "Epoch 72/80\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.0184 - val_loss: 0.0064\n",
            "Epoch 73/80\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.0176 - val_loss: 0.0062\n",
            "Epoch 74/80\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.0169 - val_loss: 0.0061\n",
            "Epoch 75/80\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.0163 - val_loss: 0.0062\n",
            "Epoch 76/80\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.0162 - val_loss: 0.0063\n",
            "Epoch 77/80\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.0163 - val_loss: 0.0064\n",
            "Epoch 78/80\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.0164 - val_loss: 0.0065\n",
            "Epoch 79/80\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.0163 - val_loss: 0.0064\n",
            "Epoch 80/80\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.0160 - val_loss: 0.0062\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(r.history['loss'],label='loss')\n",
        "plt.plot(r.history['val_loss'],label='val_loss')\n",
        "plt.legend()"
      ],
      "metadata": {
        "id": "QwvSGYTZckfD",
        "outputId": "dcf82dd2-bf08-4842-89e0-edbfba1e4a8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbd4fcd9e80>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXxU5bnA8d8zSzKBJEBIIOyLREFAUON2FbRY61KX1t2qVVultW5tra3aXmutbW/L7XarV2vVutRacGmL+9VCRVtFA7LvqEBYkwAJCVlmee4f50wYkkkySSZMJnm+n898MnPOmXOeSSbPvPOc97yvqCrGGGPSnyfVARhjjEkOS+jGGNNDWEI3xpgewhK6Mcb0EJbQjTGmh/Cl6sD5+fk6evToVB3eGGPS0qJFi8pVtSDeupQl9NGjR1NSUpKqwxtjTFoSkU0trbOSizHG9BCW0I0xpoewhG6MMT1EymroxpjeKRgMUlpaSl1dXapD6dYCgQDDhw/H7/cn/BxL6MaYQ6q0tJScnBxGjx6NiKQ6nG5JVamoqKC0tJQxY8Yk/DwruRhjDqm6ujoGDhxoybwVIsLAgQPb/S3GErox5pCzZN62jvyO0i6hr92xj1/+31oqqutTHYoxxnQraZfQN5ZV87t5GyizhG6M6aDs7OxUh9AlEk7oIuIVkY9E5OU46zJFZLaIbBCRhSIyOplBxgr4nZBrG8JddQhjjElL7Wmh3wasbmHdV4E9qjoO+DXw884G1pKAzwtAXTDSVYcwxvQSqsodd9zBpEmTmDx5MrNnzwZg+/btTJ8+nalTpzJp0iTeeecdwuEw1157beO2v/71r1McfXMJdVsUkeHA54GfAN+Os8kFwL3u/eeBB0REtAvmtwtkuAk9ZC10Y9Ldj15ayaptVUnd55FDc/nheRMT2vbFF19kyZIlLF26lPLyco477jimT5/On//8Z84880y+//3vEw6H2b9/P0uWLGHr1q2sWLECgL179yY17mRItIX+G+C7QEvN4mHAFgBVDQGVwMBORxdHtIVeH7SEbozpnHfffZcrrrgCr9fL4MGDOfXUU/nwww857rjj+OMf/8i9997L8uXLycnJYezYsXz88cfccsstvP766+Tm5qY6/GbabKGLyLnALlVdJCKndeZgIjITmAkwcuTIDu0jWkO3kosx6S/RlvShNn36dBYsWMArr7zCtddey7e//W2+/OUvs3TpUt544w0efvhh5syZw+OPP57qUA+SSAv9ZOB8EfkU+AswQ0T+1GSbrcAIABHxAf2AiqY7UtVHVLVYVYsLCuIO59umgD9aQ7cWujGmc6ZNm8bs2bMJh8OUlZWxYMECjj/+eDZt2sTgwYO54YYbuP7661m8eDHl5eVEIhEuuugi7r//fhYvXpzq8Jtps4WuqncBdwG4LfTvqOpVTTabC1wDvAdcDMzrivo5WEI3xiTPF7/4Rd577z2mTJmCiPCLX/yCwsJCnnzySWbNmoXf7yc7O5unnnqKrVu3ct111xGJONWBn/3sZymOvrkOj+UiIvcBJao6F3gMeFpENgC7gcuTFF8zjd0WreRijOmg6upqwLkac9asWcyaNeug9ddccw3XXHNNs+d1x1Z5rHYldFX9J/BP9/49McvrgEuSGVhLDnRbtBa6McbESrsrRT0eIcPnsW6LxhjTRNoldICAz0O9lVyMMeYg6ZnQ/V4ruRhjTBOW0I0xpodI04TuodYSujHGHCQtE3qW32tXihpjTBNpmdAzreRijDlEWhs7/dNPP2XSpEmHMJrWpWVCD/i91IWshW6MMbE6fKVoKgV8HnZZC92Y9PfanbBjeXL3WTgZzv6vFlffeeedjBgxgptuugmAe++9F5/Px/z589mzZw/BYJD777+fCy64oF2Hraur48Ybb6SkpASfz8evfvUrPvOZz7By5Uquu+46GhoaiEQivPDCCwwdOpRLL72U0tJSwuEw//mf/8lll13WqZcN6ZrQreRijOmgyy67jG9+85uNCX3OnDm88cYb3HrrreTm5lJeXs6JJ57I+eef366Jmh988EFEhOXLl7NmzRo+97nPsW7dOh5++GFuu+02rrzyShoaGgiHw7z66qsMHTqUV155BYDKysqkvLY0TejWy8WYHqGVlnRXOfroo9m1axfbtm2jrKyMAQMGUFhYyLe+9S0WLFiAx+Nh69at7Ny5k8LCwoT3++6773LLLbcAMH78eEaNGsW6des46aST+MlPfkJpaSkXXnghRUVFTJ48mdtvv53vfe97nHvuuUybNi0pry0ta+jWy8UY0xmXXHIJzz//PLNnz+ayyy7jmWeeoaysjEWLFrFkyRIGDx5MXV1dUo71pS99iblz55KVlcU555zDvHnzOPzww1m8eDGTJ0/mBz/4Affdd19SjpWmLXQruRhjOu6yyy7jhhtuoLy8nLfffps5c+YwaNAg/H4/8+fPZ9OmTe3e57Rp03jmmWeYMWMG69atY/PmzRxxxBF8/PHHjB07lltvvZXNmzezbNkyxo8fT15eHldddRX9+/fn0UcfTcrrSsuEnun3Uh+KoKrtqnEZYwzAxIkT2bdvH8OGDWPIkCFceeWVnHfeeUyePJni4mLGjx/f7n1+4xvf4MYbb2Ty5Mn4fD6eeOIJMjMzmTNnDk8//TR+v5/CwkLuvvtuPvzwQ+644w48Hg9+v5+HHnooKa9LumgeijYVFxdrSUlJh577v//cwC9eX8uaH5/VOOGFMSY9rF69mgkTJqQ6jLQQ73clIotUtTje9mlZQ7cx0Y0xprlEJokOAAuATHf751X1h022uRaYhTO3KMADqpqcolAc0VZ5bTBM/646iDHGuJYvX87VV1990LLMzEwWLlyYoojiS6SGXg/MUNVqEfED74rIa6r6fpPtZqvqzckPsbmsDOeLhfV0MSY9pdv5r8mTJ7NkyZJDesyOlMPbLLmoo9p96HdvqSm8u6zkYkz6CgQCVFRUdChh9RaqSkVFBYFAoF3PS6iXi4h4gUXAOOBBVY33PeMiEZkOrAO+papb4uxnJjATYOTIke0KNFa05GIJ3Zj0M3z4cEpLSykrK0t1KN1aIBBg+PDh7XpOQgldVcPAVBHpD/xVRCap6oqYTV4CnlXVehH5GvAkMCPOfh4BHgGnl0u7Io2R6beSizHpyu/3M2bMmFSH0SO1q5eLqu4F5gNnNVleoar17sNHgWOTE158jS10myjaGGMatZnQRaTAbZkjIlnAGcCaJtsMiXl4PrA6mUE2Fa2h11vJxRhjGiVSchkCPOnW0T3AHFV9WUTuA0pUdS5wq4icD4SA3cC1XRUwQFbGgW6LxhhjHG0mdFVdBhwdZ/k9MffvAu5KbmgtC1gN3RhjmrErRY0xpodIz4Te2G3RWujGGBOVlgk90xctuVgL3RhjotIyoXs8QobPY90WjTEmRlomdHBnLWqwhG6MMVFpm9ADfo/V0I0xJkYaJ3SvlVyMMSZG+iZ0n80raowxsdI3oVvJxRhjDpK2CT3Tby10Y4yJlbYJPcsSujHGHCRtE7qVXIwx5mBpnNCtl4sxxsRK34RuvVyMMeYg6ZvQreRijDEHSWTGooCIfCAiS0VkpYj8KM42mSIyW0Q2iMhCERndFcHGCthJUWOMOUgiLfR6YIaqTgGmAmeJyIlNtvkqsEdVxwG/Bn6e3DCbC/i91IciRCIdnmvaGGN6lDYTujqq3Yd+99Y0i14APOnefx44XUQkaVHGER0TvT5kZRdjjIEEa+gi4hWRJcAu4E1VXdhkk2HAFgBVDQGVwMA4+5kpIiUiUlJWVtapwA9MQ2dlF2OMgQQTuqqGVXUqMBw4XkQmdeRgqvqIqharanFBQUFHdtGocdYi67pojDFAO3u5qOpeYD5wVpNVW4ERACLiA/oBFckIsCU2UbQxxhwskV4uBSLS372fBZwBrGmy2VzgGvf+xcA8Ve3Ss5U2UbQxxhzMl8A2Q4AnRcSL8wEwR1VfFpH7gBJVnQs8BjwtIhuA3cDlXRaxK5BhCd0YY2K1mdBVdRlwdJzl98TcrwMuSW5orYu20GstoRtjDJDmV4oC1FsN3RhjgLRO6FZyMcaYWOmf0K3bojHGAGmd0K3bojHGxErbhJ5lJRdjjDlI2ib0aMnFerkYY4wjbRN6ps9KLsYYEyttE7qIkOnzUG8tdGOMAdI4oYNNcmGMMbHSPKHbNHTGGBOV1gk9y++1fujGGONK64Qe8HupbbCEbowxkOYJPdPvpc6moDPGGCDNE3rA57GTosYY40rvhO73WrdFY4xxJTJj0QgRmS8iq0RkpYjcFmeb00SkUkSWuLd74u0r2ayXizHGHJDIjEUh4HZVXSwiOcAiEXlTVVc12e4dVT03+SG2zHq5GGPMAW220FV1u6oudu/vA1YDw7o6sERYLxdjjDmgXTV0ERmNMx3dwjirTxKRpSLymohMbOH5M0WkRERKysrK2h1sU3alqDHGHJBwQheRbOAF4JuqWtVk9WJglKpOAX4H/C3ePlT1EVUtVtXigoKCjsbcKNPvsW6LxhjjSiihi4gfJ5k/o6ovNl2vqlWqWu3efxXwi0h+UiONI+Dz0hCKEIloVx/KGGO6vUR6uQjwGLBaVX/VwjaF7naIyPHufiuSGWg80THR662VbowxCfVyORm4GlguIkvcZXcDIwFU9WHgYuBGEQkBtcDlqtrlzeasxmnowmRleLv6cMYY0621mdBV9V1A2tjmAeCBZAWVKJso2hhjDkj7K0UB67pojDGkfUK3aeiMMSYqrRN6ppVcjDGmUVon9IDPTeh2cZExxqR3Qo/2bKm3kosxxqR3Qg/EdFs0xpjeLr0TultyqbWEbowxaZ7QoydFreRijDHpntCt5GKMMVFpntCt26IxxkSldULP9NmFRcYYE5XWCV1ECPg9NlG0McaQ5gkd3GnoLKEbY0wPSOg+m4bOGGOgJyR0v8dq6MYYQ2IzFo0QkfkiskpEVorIbXG2ERH5HxHZICLLROSYrgm3OZso2hhjHInMWBQCblfVxSKSAywSkTdVdVXMNmcDRe7tBOAh92eXC/i9NlG0McaQQAtdVber6mL3/j5gNTCsyWYXAE+p432gv4gMSXq0cTglF2uhG2NMu2roIjIaOBpY2GTVMGBLzONSmid9RGSmiJSISElZWVn7Im2BlVyMMcaRcEIXkWzgBeCbqlrVkYOp6iOqWqyqxQUFBR3ZRTPWy8UYYxwJJXQR8eMk82dU9cU4m2wFRsQ8Hu4u63LWy8UYYxyJ9HIR4DFgtar+qoXN5gJfdnu7nAhUqur2JMbZIiu5GGOMI5FeLicDVwPLRWSJu+xuYCSAqj4MvAqcA2wA9gPXJT/U+CyhG2OMo82ErqrvAtLGNgrclKyg2sO6LRpjjKNHXCnaEIoQiWiqQzHGmJRK+4Senel8yahpCKU4EmOMSa20T+g5ASehV9VZQjfG9G49IKH7AdhXF0xxJMYYk1ppn9Bz3YReVWstdGNM75b2CT1acrEWujGmt0v7hJ6bFS25WAvdGNO7pX1CP3BS1FroxpjercckdGuhG2N6u7RP6Jk+L5k+j7XQjTG9XtondHC6LlovF2NMb9cjEnpuwGe9XIwxvV6PSOg5WX67UtQY0+v1iIRuLXRjjOkhCT0n4LNeLsaYXi+RGYseF5FdIrKihfWniUiliCxxb/ckP8zW5Qb8VNVaC90Y07slMmPRE8ADwFOtbPOOqp6blIg6wFroxhiTQAtdVRcAuw9BLB2WG/BTGwwTDNvMRcaY3itZNfSTRGSpiLwmIhOTtM+E2dWixhiTnIS+GBilqlOA3wF/a2lDEZkpIiUiUlJWVpaEQztsTHRjjElCQlfVKlWtdu+/CvhFJL+FbR9R1WJVLS4oKOjsoRtFR1y0q0WNMb1ZpxO6iBSKiLj3j3f3WdHZ/baHjYlujDEJ9HIRkWeB04B8ESkFfgj4AVT1YeBi4EYRCQG1wOWqql0WcRw2r6gxxiSQ0FX1ijbWP4DTrTFlGqehsxa6MaYX6xFXiuYGbNYiY4zpEQk9O1pysatFjTG9WI9I6F6PkJ1pV4saY3q3HpHQIXr5v7XQjTG9V49J6LkBv50UNcb0aj0modsAXcaY3q5HJXRroRtjerMek9Bzs/zWQjfG9Go9JqFbycUY09v1mIQenbXoEI86YIwx3UaPSeg5AT+hiFIXtEkujDG9Uw9K6DbiojGmd+sxCb1xTHRL6MaYXqrHJHQbQtcY09v1mISe20UDdNlJVmNMuuhBCT35Q+j+fclWTvn5fDaWVSdtn8YY01XaTOgi8riI7BKRFS2sFxH5HxHZICLLROSY5IfZtpwkJ/SPy6q568XlbN1by/f/utxa6saYbi+RFvoTwFmtrD8bKHJvM4GHOh9W+x2ooXe+5FIfCnPLsx+R4fNw2+lFvP/xbp5fVNrp/RpjTFdqM6Gr6gJgdyubXAA8pY73gf4iMiRZASaqT4YXr0eS0m1x1utrWbmtilkXT+G204soHjWAn766mt01DUmI1BhjukYyaujDgC0xj0vdZc2IyEwRKRGRkrKysiQc+qB9OwN01Xau5DJ/7S4effcTrjlpFGccORiPR/jphZOprg9x/yurkhStMcYk3yE9Kaqqj6hqsaoWFxQUJH3/uQF/p1roe/c38J05SxlfmMNd50xoXH744By+Nv0wXly8lX9vKE9GqMYYk3TJSOhbgRExj4e7yw65zg7Q9c76cipqGvjJFycR8HsPWnfzjHGMHtiHH/xtBZGInSA1xnQ/yUjoc4Evu71dTgQqVXV7Evbbbp0dE33Rpj1k+b1MGd6/2bqA38stM4r4uLyGFdsqOxOmMcZ0CV9bG4jIs8BpQL6IlAI/BPwAqvow8CpwDrAB2A9c11XBtiU34Gfz7v0dfv7izXuYOqI/Pm/8z7nPjB+ER+Ct1bs4Kk7SN8aYVGozoavqFW2sV+CmpEXUCTmBjk9ysb8hxMptVdx46mEtbpPXN4NjRg5g3pqdfPuMwzsapjHGdIkec6UouCWXDl76v6y0knBEOWZU6y3v0ycMZsXWKnZU1nXoOMYY01V6VELPzfJT3RDq0EnLRZv2AHD0iAGtbvfZCYMA+Meane0P0BhjulDPSugBH6qwr779ZZePNu/hsIK+DOib0ep24wZlMyIvi3mrd3U0TGOM6RI9LKFHx3NpX9lFVVm0aQ/Hjmq9dQ7OBUynjx/MuxvKqW0IdyhOY4zpCj0qoR+Ytah9LfRPymvYsz+YUEIHOH3CIOpDEf690S4yMsZ0H+mX0GvKYe3rEKpvtio64mJ7T4xG6+eJJvQTxgykb4aXt6zsYozpRtIvoX/yNjx7GVRsaLYqN6tjLfTFm/eQG/AxNj87oe0zfB6mH17AvDU7bVhdY0y3kX4JPd/t/12+rtmqxhZ6O2voizbt4ZhRA/B4JOHnnD5hMDur6lm5rapdxzLGmK6Sfgk9z73wp3x9s1UdqaFX1gZZt7OaY0cmVm6JOu2IAkTgrdXWfdEY0z2kX0LP6AP9RraR0BNvoX+0uX3186j87EyOHtGfeWusjm6M6R7SL6ED5BfFLblk+rxk+jxUtaOFvnjzXjwCU0a0f2yW044YxPKtlTbxhTGmW0jjhL4e4pyQzM1q35joizftYXxhLn0z2xzWpplpRfmowr9sjHRjTDeQvgk9WANV25qtcobQTayFHo4oH21O7IKieI4a3p/cgI931id39iVjjOmINE3obk+Xinh1dH/C/dA/LqumpiHM1A6UWwC8HuGUonzeWV9u3ReNMSmX3gk9zonR3HbMWhTtcjhxWG6HQ5lWVMD2yjo2llV3eB/GGJMMCSV0ETlLRNaKyAYRuTPO+mtFpExElri365MfaozswZCRE/fEaG7An3A/9FXbq8jweTisILELiuI5ZVw+4ExfZ4wxqdRmQhcRL/AgcDZwJHCFiBwZZ9PZqjrVvT2a5DibBnXgxGgTBTmZ7KisS2gI3ZXbKjlicA7+FmYoSsSIvD6Mze9rCd0Yk3KJZLLjgQ2q+rGqNgB/AS7o2rASkH943IQ+blA2+xvCbK9qfQIKVWXltiomDu14uSVqWlE+722soD5koy8aY1InkYQ+DNgS87jUXdbURSKyTESeF5ER8XYkIjNFpERESsrKOtkzJH8cVJVCQ81Bi8cNcsonG3a1XtPeVlnH3v3BpCT0U4oKqA2GWbxpb6f3ZYwxHZWsk6IvAaNV9SjgTeDJeBup6iOqWqyqxQUFBZ07YmNPl4MH6SpyE/r6nftaffoq94TokUP7dS4O4MSxefg8Yt0XjTEplUhC3wrEtriHu8saqWqFqkbHs30UODY54bWihZ4uA7MzGdDH32avk5XbKhGB8YU5nQ4lJ+DnmJEDrI5ujEmpRBL6h0CRiIwRkQzgcmBu7AYiMiTm4fnA6uSF2IK8sSCeuD1dxg3KbrPksnJbFWPy+3boCtF4phXls2KbDQNgjEmdNhO6qoaAm4E3cBL1HFVdKSL3icj57ma3ishKEVkK3Apc21UBN/JlQv9RLST0HNbvqm71Yp9V26qYmIRyS9S0wwtsGABjTEol1DxV1VeBV5ssuyfm/l3AXckNLQH5h0N584kuxg3KZu/+IBU1DeRnZzZbv3d/A1v31nLViaOSFsrkYf3ol+Xn7XVlnDdlaNL229SemgbmrdnFvLW7GJIb4GunHkZBTvPXaIzpfZJTb0iV/CJnBqNIBDwHvmzE9nSJl9CjJ0Sb9XDZtRpWvwzjPw+D43W1b5nXI5w+fhBvrtpJfShMps/bzhfTun+u3cXv3/6YDz7dTTiiFORksrumgT9/sJmvnDyGG6aPpV+WP6nHNMakl/S89D8qvwhCdVC55aDFRW10XVzZNKHvWA5zvgz/exLMvx8ePhn+fjNUbW9XOOdNHUplbZAF65Jbdvn3hnJueKqErXtr+fqpY5l788l8cPfpvPmt6Zw+YTAPzN/AtJ/P49Xl7YvXGNOzpHlCjz9I15B+AfpmeFtM6Ku2V1GYG2BgAHjuWnj4FNg4H6Z/B25ZDCfcCEv/Av9zNMz/KUQSu2DolHH55PXN4O9Ltra9cYJWbqtk5tOLGJufzUu3nMIdZ47nqOH9ERHGFmTzuyuO5pVbT2FsQTa3PvsRb6+zrpPG9FbpndAHFjk/m3RdFBEOa6Wny8ptlRw5NBfe+RWs/CtMux2+uQxm/AAGHgZn/RRuKYHx58DbP4c37k4oHL/Xw+cnD+Gt1Tuprm/fRNXxbNm9n2v/+CG5AR9PfOW4FksqE4f246mvHk/R4By+/vSixlmYjDG9S3on9L75EOgfv6dLQfyEXhcMs7GshlP77YJ3fgmTL4XT74GsJmOiDxgNFz8OJ90MCx+G9x9KKKQLpg6lLhjhzVU7OvKKGu2uaeCaxz+gIRThya8cz5B+Wa1unxvw8+RXjmNQbibXPfFhmxdWGWN6nvRO6CItj+kyOJsdVXXNRl5cu2MfGglzweafQSAXzvqv1o9xxo9h/Lnw+l3OCdM2HDNyAMP6ZzF3SfPJNxKlqtw+Zwlb99by2DXFFA1O7OKnQTkBnv7KCfi9Hq5+7AO27a3tcAzGmPST3gkdWhx1cZw7JO7GJq30lduquM77Gv33LIezfwF9B7a+f48HLvwDDDsGXrgeti5qY3Ph/KlDWbC+nIrq+la3bclzi0qZv7aMu84eT/HovHY9d+TAPjz1leOpqQ8x8+kSahtswDBjeov0T+iDJ0L1DqjYeNDilgbp2v7JKr7jfw494myYdFFix8joA1f8BbIHwZ8vh8rWT3peMHUo4Yjy6or2l122V9by45dWccKYPL580uh2Px9gwpBcfnP5VFZuq+KuF5fZbErG9BLpn9AnftEZAuCjPx20eGReHzK8noMTuipnbPwpKj7k879ySjaJyh4EX5oDwf0w52oItjw87/jCXI4YnMPcdvZ2UVXufGE5oYgy6+IpeDztiK+J0ycM5vYzDudvS7bx6DufdHg/xpj0kf4JPXcoFH0OlvwZwgd6lvi8Hsbk9z0ooW9/fw5HBZfy7qibnOe116Dx8MWHnbLLq7dDKy3f86cO5cNP91C6Z3/Cu3+upJS315Vx59njGTmwT/vja+Kmz4zj7EmF/Oy11Sw4hN0ZE5lcxBiTfOl9pWjU0VfDutdhw5twxNmNi8cNymbFtkoANFiH560fsp4RHHvhtzp+rAnnwfQ7YMEsGHo0HBd/tr3zpwxl1htrmf3hFm7/3BFt7nbb3lp+/LJTarn6hJHw6b9gw1tQWercqkrB43dq+cOOdW5DpoIvo8V9igj/fckUPimv4ZZnP+K5r5/E4QmeYE1EJKIsWF/G3KXbKN1TS/m+esr21VPTEGJ8YS7Fowdw7KgBHD8mr81eOsaYzusZCf3wM6HvIFj8dLOE/tqK7dQFw2x66b85IrydJcc8RFFu384d77S7YftSeO17MGgijDqp2SYj8vpw7lFDeOifG/nshMFMGdG/xd3V1Ie44akSfFrPgxNX4/nDHc7Vqx6f802i3wgYeZIzmcen/4LlzzlP7DvI+UApvs4pCcXRN9PHI1cXc/HD/+aKR97nmRtOYHxh5yb1KK+uZ07JFp79YDNbdtcyoI+fokE5TBiay/TsTAJ+L8u37uX5RaU89d4mROCzEwbz1VPGcMKYPKQ9pS5jTMIkVSfMiouLtaSkJHk7fPMe+PcD8O1VkFMIwEtLt3HLsx/x0nWHM+bZ6azwHcmxd73ZqTlEG9XuhT/MgNrdcNULTou5icr9Qc7+7QIyfB5euXVa3KF6wxHl6099wJD1z/KD7Llk1FdAwQQ48etOH/mMOKWXqm2w5QPnvMGGN8GbAZMuhv+4pcUxaD4uq+aKP7xPMKw8c/0JTBjS/qS+ry7IQ//cyGPvfkJ9KMKJY/O48oRRfG7i4Lhj14TCEdbs2MfrK3bwzMJN7HFniLp+2hjOPWpocv4OxvQyIrJIVYvjrusxCb18PTxQDJ+9F05xSiqrt1dx9m/f4X/7/Ykz6t5g+fmvccyxJyTvmHs+hSfPh/0VTi+YMdOabfLBJ7u5/JH3uOiY4cy6ZEqz9X+cPYfjVv6ESZ5PYfQ0Z/iBMacmfoc2I8UAAA2/SURBVMK2bB188HvnHEKwFiZ+AaZ/N25i/6S8hiseeZ/6UJg/XX9CwsMHB8MR/vLBZn7z1noqahr4wtSh3DyjqLEnUSLqgmH++tFWHn/3E9bvqmZY/yy+dupYLi0eQcCf3IHMwCkHldfUU9sQJsvvJZDhJcvvtQ8Rk/Z6R0IHePxsqNkFN5eACHXBMF/44R94xX8nb/f/AjO+9URyjwdOa/mpL8DeTXDpU075p4lf/t9afjdvA7+74ugDQ+tWbWfDnLsZV/oiVf58ci+Y5fbY6WA5Yv9ueO8BWPh7pzRz5AVOi33YsQftc1OFk9T31YW4ftpYrj15dItDCuyrC/JcSSlP/PtTNu/ez4lj8/j+OUcyeXjHx5FXVeat2cWD8zewePNe8rMzuPKEUVwwdShjCxL/gIgKhiOs27mPZaWVLCvdy9od+9hRWceuffWE4pyc7Zfl57CCvhxWkM3YgmwmDMlh8rB+DIwzKqcx3VGnE7qInAX8FvACj6rqfzVZnwk8hTP1XAVwmap+2to+uyShL/kz/O1GuO41Z5yXZX9h25u/o0+kmpqZHzBs2PDkHi+qpgL+dCHsXAFn3AfHfBkyD5x8DIYjXPr799iwq5oLi3wcv+1pPlvzCh4N81a/Cznj67/E1ydJk23s3w3vPegm9n1QeBQc91WYfAlkOOcOtuzez49eWsVbq3eSk+nj2pNHc9ExwwmGI1TXh6ipD/OPNTt5rqSUmvoGzhlWz/UTQkztuxvZ/bHzzaS+yunCGayFUANkZkNWHvTJc4ZkGDDGGRcn7zDIG+NMSBJDVVn4yW4e+udGFqwvQ9UZ/fK8KUM5fkwewwdkUZCd2VhvV1Wq60PsrKpjxdYqlpbuZVlpJSu2VlIfigBOsp4wJIeh/bIo7BegsF+Avhk+6kJhahuc246qOjaWVbOxrIayfQcu/BrWP4tJw3IZX5hL0eBsigblMCa/Lxm+tlv0qsq++hAV1Q2UV9dTUV1PWXUDVbVBahvC1DSEGi/w8noEv9eD3yvkBPz07+OnX5af/n0yGNg3g4KcTPL6Ztg3CdOiTiV0EfEC64AzgFKcKemuUNVVMdt8AzhKVb8uIpcDX1TVy1rbb5ck9IYa+OV4yMh2WuqRELsHTGHNhFv4j89dktxjNVVX6QzB+/E/neMfdSkce52T3HZ/QsWWtbyz4E3OCr6FnxAf9juTVUUzufSM6WQnaRq8g+OpgmWzoeRx2LXKiWnECe7teCg8ilUVQR58exOvrCwjQAPDpYyRsosRUsbhnq38R/Z2RoY+xRusObDfzFxnnJs+eeDLAn+WU8Ov3+ecT9i/2/nd18YMECYeZ3ap/MOdK3vzxkLuMOeEb+5QdjRk8fKKnby0bDtLt+x1n6T084U4LBeygnvw1FXQL7yXfKkkT6oY5KlmdFYthRm19PMG6eupxx+pQ8JB52Syx+v89AUg0O/ALWsA9BkIfQZS4xvAJ/v9rNnrYWm5h5KdEdbuDhFRJ5l6xPmQGNA3gwF9MsgJ+AhHlGA4QigUJthQR01NNbX79yORejIJ4iWCnzA+QgiAx0Omz0uG30cYL3URL7URLzVhD/uCHhrwU4+fEF7gwDep3ICP/n0y6JflJPzsTB8ZPg8ZPg9+rwcRCIeVYCRCOKLUByONH1z1wRANwRD1oYhzPxQhrBBSCKug6nTr9XujHy4eAn6PU5pyb859D1kZXjJ9Xnwewe/z4PfIQddHqDofaKGIElYlHHZ/RtxlYUVRIgoRN9d4RPCI+9Mj+DyCz+PB5xXnA88j+LzOY59H8Iiz3OsRRKTxtyTiHh8nhmgssceKR2KPL85jryd2WfRYB7YVpPGLbrzv0G01i4XoF2VnP2Pz+yY8pEec+DuV0E8C7lXVM93HdwGo6s9itnnD3eY9EfEBO4ACbWXnXZLQAd66F5Y8C0ddAlOvcvqOHyqqTh/1Dx+DlS86Y7XH8vick5enftdpvR6qmDa/D8vnwOaFTnJv8+0HkYwcPIWToXAyFE6C/COcmPsMTKwsVLsHKj6G3Rud8xsV692fG5r/XgDEC74AYW8GGmrAG9qPtBCnigey8pC++c63goy+zgeLvw94/aARiIScW7DW+XCrq4S6vU5cDa3PNxvxZhKWDBrET0SjySKCqOIniF+D+Oj8aJoHvSaEiMdPWPyExUcILxGEsDq3iILgxCBEEBTnIyKCl7B7i+Al0uaxInhQ8RDBQ0S8RPC4e/A03iLqHCWiOEdTJ0pU3eSkCM7NE3MTFI84fzdn/cGvMbokggd1fzrHcY/nHuvAq2z+Xou+Lw78PHh5S79f9xU0iyXe47aO2RmKsHn0xcz4yo879PzOJvSLgbNU9Xr38dXACap6c8w2K9xtSt3HG91typvsayYwE2DkyJHHbtq0qUMvKC3s3+0MzasRp+QwYAz0H+kknFSqq3Q+dHatgXADhIMQCTpx9R8NA0Y5LfC+BR2v57cmEoZ9O2Dfduf8Q9U2J6ZwPYTqnZg8fidJR2998px4oresAU4LvKOCdc6J7P3lToKv3esm+71uCanWiaXxg0ec34V4wJvp9P33BZxvJv4sp5wUfezxOTev33ke6rwHoh8y4aDzGkP1zv1QXcxrDx5YHwkeeJ7i/PR4nBiIxuJ3jiWeA99KxOv+9MTEzoF9aNj5G0TjafzwC7vrQs4MYLFxqza+FxQ9cHwRQJDG48X8nqLJMfoeiuYZje47Zv/Rx5GwcwT3A1QjEecDFXU30Zh06qTWxi6w0Za7eNyfja+exjQefb7TlG+8rxo5sO/GFn+k8RiN4Uv0F9nk2DExxaWRxn1HY9HDz6b/iVfF374NrSX0Q9oPXVUfAR4Bp4V+KI99yPXJc2rX3U2gHxw2w7mlgscL/YY5t1TxB1IfQ5o6FFcQSMxxkt//qWdL5MzLVmBEzOPh7rK427gll344J0eNMcYcIokk9A+BIhEZIyIZwOXA3CbbzAWuce9fDMxrrX5ujDEm+dosuahqSERuBt7A+Qb0uKquFJH7gBJVnQs8BjwtIhuA3ThJ3xhjzCGUUA1dVV8FXm2y7J6Y+3VAF/cLNMYY0xq7esEYY3oIS+jGGNNDWEI3xpgewhK6Mcb0ECkbbVFEyoCOXiqaD5S3uVVqdNfYumtcYLF1RHeNC7pvbN01LmhfbKNUtSDeipQl9M4QkZKWLn1Nte4aW3eNCyy2juiucUH3ja27xgXJi81KLsYY00NYQjfGmB4iXRP6I6kOoBXdNbbuGhdYbB3RXeOC7htbd40LkhRbWtbQjTHGNJeuLXRjjDFNWEI3xpgeIu0SuoicJSJrRWSDiNyZ4lgeF5Fd7oxN0WV5IvKmiKx3fw5IQVwjRGS+iKwSkZUiclt3iE1EAiLygYgsdeP6kbt8jIgsdP+ms91hmlNCRLwi8pGIvNydYhORT0VkuYgsEZESd1l3eK/1F5HnRWSNiKwWkZO6SVxHuL+r6K1KRL7ZTWL7lvv+XyEiz7r/F0l5n6VVQncnrH4QOBs4ErhCRI5MYUhPAGc1WXYn8A9VLQL+4T4+1ELA7ap6JHAicJP7e0p1bPXADFWdAkwFzhKRE4GfA79W1XHAHiCVUz3dBqyOedydYvuMqk6N6a+c6r8nwG+B11V1PDAF53eX8rhUda37u5oKHAvsB/6a6thEZBhwK1CsqpNwhiS/nGS9z5z5+9LjBpwEvBHz+C7grhTHNBpYEfN4LTDEvT8EWNsNfm9/B87oTrEBfYDFwAk4V8j54v2ND3FMw3H+yWcAL+PMhNZdYvsUyG+yLKV/T5yZyT7B7VzRXeKKE+fngH91h9iAYcAWIA9n+PKXgTOT9T5LqxY6B34ZUaXusu5ksKpud+/vAAanMhgRGQ0cDSykG8TmljSWALuAN4GNwF5VDbmbpPJv+hvgu0DEfTyQ7hObAv8nIovcydYh9X/PMUAZ8Ee3TPWoiPTtBnE1dTnwrHs/pbGp6lbgv4HNwHagElhEkt5n6ZbQ04o6H7cp6xcqItnAC8A3VbUqdl2qYlPVsDpfg4cDxwPjD3UM8YjIucAuVV2U6lhacIqqHoNTbrxJRKbHrkzR39MHHAM8pKpHAzU0KWF0g/+BDOB84Lmm61IRm1uzvwDnw3Ao0JfmZdsOS7eEnsiE1am2U0SGALg/d6UiCBHx4yTzZ1T1xe4UG4Cq7gXm43y97O9OLg6p+5ueDJwvIp8Cf8Epu/y2m8QWbdmhqrtwasHHk/q/ZylQqqoL3cfP4yT4VMcV62xgsarudB+nOrbPAp+oapmqBoEXcd57SXmfpVtCT2TC6lSLnTD7Gpz69SElIoIzz+tqVf1Vd4lNRApEpL97Pwunrr8aJ7FfnKq4AFT1LlUdrqqjcd5X81T1yu4Qm4j0FZGc6H2cmvAKUvz3VNUdwBYROcJddDqwKtVxNXEFB8otkPrYNgMnikgf9/80+jtLzvsslScrOnhS4RxgHU7t9fspjuVZnDpYEKe18lWcuus/gPXAW0BeCuI6Beer5DJgiXs7J9WxAUcBH7lxrQDucZePBT4ANuB8Nc5M8d/1NODl7hKbG8NS97Yy+r5P9d/TjWEqUOL+Tf8GDOgOcbmx9QUqgH4xy1IeG/AjYI37P/A0kJms95ld+m+MMT1EupVcjDHGtMASujHG9BCW0I0xpoewhG6MMT2EJXRjjOkhLKEbY0wPYQndGGN6iP8HWfhCjgPkxdQAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "validation_target= Y[-N//2:]\n",
        "validation_predictions=[]\n",
        "\n",
        "i=-N//2\n",
        "\n",
        "while(len(validation_predictions)<len(validation_target)):\n",
        "  p=model.predict(X[i].reshape(1,-1))[0,0]\n",
        "  i+=1\n",
        "  validation_predictions.append(p)"
      ],
      "metadata": {
        "id": "zf0lMnHGc6BR",
        "outputId": "c4c629d6-cdd3-4141-b2f8-020debcb7bc0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(validation_target,label='forecast_target')\n",
        "plt.plot(validation_predictions,label='forecast_predictions')\n",
        "plt.legend()"
      ],
      "metadata": {
        "id": "3-RmvCr-eDxL",
        "outputId": "2897690e-1aa8-41c8-deff-19af66a0a827",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbd4fda6ca0>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "validation_target= Y[-N//2:]\n",
        "validation_predictions=[]\n",
        "\n",
        "last_x=X[-N//2]\n",
        "\n",
        "while(len(validation_predictions)<len(validation_target)):\n",
        "  p=model.predict(last_x.reshape(1,-1))[0,0]\n",
        "  validation_predictions.append(p)\n",
        "\n",
        "  last_x=np.roll(last_x,-1)\n",
        "  last_x[-1]=p"
      ],
      "metadata": {
        "id": "hru_0L-2edLf",
        "outputId": "7a49be8e-831c-4c3d-a5da-0d7539c58008",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(validation_target,label='forecast_target')\n",
        "plt.plot(validation_predictions,label='forecast_predictions')\n",
        "plt.legend()"
      ],
      "metadata": {
        "id": "0cBUFGhJe5lJ",
        "outputId": "f892522e-6227-4312-ee60-23b5bdfd1f17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbd4fbbbf10>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "G63ZpKL9e8Sb"
      },
      "execution_count": 26,
      "outputs": []
    }
  ]
}