y_hat_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 20)\n"
     ]
    }
   ],
   "source": [
    "# Predict using testing set\n",
    "\n",
    "dataset_total = pd.concat((train, test), axis = 0)\n",
    "inputs = dataset_total[len(dataset_total) - len(test) - batch_size:].values\n",
    "inputs = inputs.reshape(-1,1)\n",
    "inputs = sc.transform(inputs)\n",
    "x_test = []\n",
    "prediction_window = 1000\n",
    "for i in range(batch_size, batch_size+prediction_window):\n",
    "    x_test.append(inputs[i-batch_size:i, 0])\n",
    "x_test = np.array(x_test)\n",
    "print(x_test.shape)\n",
    "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat = model.predict(x_test)\n",
    "y_hat = sc.inverse_transform(y_hat)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}