Time_Series_Forecasting_With_LSTMs.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align='center'>Time Series Forecasting with LSTMs</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# This relates to plotting datetime values with matplotlib:\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the dataset\n",
    "We'll take advantage of pandas' built-in <a href='https://pandas.pydata.org/pandas-docs/stable/timeseries.html'>DatetimeIndex</a> by passing <tt>parse_dates=True</tt>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "325"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../data/Alcohol_Sales.csv',index_col=0,parse_dates=True)\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "325"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Always a good idea with time series data:\n",
    "df.dropna(inplace=True)\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S4248SM144NCEN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992-01-01</th>\n",
       "      <td>3459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-01</th>\n",
       "      <td>3458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-03-01</th>\n",
       "      <td>4002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-04-01</th>\n",
       "      <td>4564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-05-01</th>\n",
       "      <td>4221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            S4248SM144NCEN\n",
       "DATE                      \n",
       "1992-01-01            3459\n",
       "1992-02-01            3458\n",
       "1992-03-01            4002\n",
       "1992-04-01            4564\n",
       "1992-05-01            4221"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S4248SM144NCEN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-09-01</th>\n",
       "      <td>12396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>13914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>14174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>15504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>10718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            S4248SM144NCEN\n",
       "DATE                      \n",
       "2018-09-01           12396\n",
       "2018-10-01           13914\n",
       "2018-11-01           14174\n",
       "2018-12-01           15504\n",
       "2019-01-01           10718"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "plt.title('Beer, Wine, and Alcohol Sales')\n",
    "plt.ylabel('Sales (millions of dollars)')\n",
    "plt.grid(True)\n",
    "plt.autoscale(axis='x',tight=True)\n",
    "plt.plot(df['S4248SM144NCEN'])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparartion\n",
    "In the next steps we'll divide the data into train/test sets, then normalize the training values so that they fall between -1 and 1 (to improve training). We'll train the model, then predict into a period that matches the test set. Finally, we'll forecast into an unknown future."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract values from the source .csv file\n",
    "y = df['S4248SM144NCEN'].values.astype(float)\n",
    "\n",
    "# Define a test size\n",
    "test_size = 12\n",
    "\n",
    "# Create train and test sets\n",
    "train_set = y[:-test_size]\n",
    "test_set = y[-test_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of train_set : (313,)\n",
      "shape of test_set : (12,)\n"
     ]
    }
   ],
   "source": [
    "print(f\"shape of train_set : {train_set.shape}\")\n",
    "print(f\"shape of test_set : {test_set.shape}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's worth noting that in our previous exercise the train and test sets were tensors. Here they're numpy arrays. This is because one of the steps we're about to perform outputs an array, and we'd have to turn it into a tensor anyway."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalize the data\n",
    "The formula for normalizing data around zero is:<br>\n",
    "### $X_{norm} = \\frac{X - \\mu} {\\sigma}$\n",
    "where $\\mu$ is the population mean, and $\\sigma$ is the population standard deviation.<br>\n",
    "Recall that back in the CNN section we transformed image files using <a href='https://pytorch.org/docs/stable/torchvision/transforms.html#torchvision.transforms.Normalize'><tt><strong>torchvision.transforms.Normalize(<em>mean, std</em>)</strong></tt></a>, both because it was built into DataLoader, and because our pretrained models expected specific normalization values.<br>\n",
    "\n",
    "Ultimately we want to perform <a href='https://en.wikipedia.org/wiki/Feature_scaling'>min/max feature scaling</a> so that our values fall between -1 and 1, as this makes hyperparameters converge faster.<br>The formula for this would be:<br>\n",
    "### $X^{\\prime} = a + \\frac{(X - X_{min}) (b - a)} {X_{max} - X_{min}}$\n",
    "where $a={-1}$ and $b=1$<br>\n",
    "\n",
    "We can use scikit-learn to do this, with <a href='https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html'><tt><strong>sklearn.preprocessing.MinMaxScaler()</strong></tt></a>\n",
    "\n",
    "NOTE: We only want to normalize the training set to avoid data leakage. If we include the test set then the higher average values of the test set could become part of the signal in the training set. There's a good article on data leakage <a href='test_set = input_data(test_norm,window_size)'>here</a>.\n",
    "\n",
    "After using transformed data to train the model and generate predictions, we'll inverse_transform the predicted values so that we can compare them to the actual test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.25.2\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Instantiate a scaler with a feature range from -1 to 1\n",
    "scaler = MinMaxScaler(feature_range=(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize the training set\n",
    "train_norm = scaler.fit_transform(train_set.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.92683135],\n",
       "       [-0.92700231],\n",
       "       [-0.83400291],\n",
       "       [-0.73792632],\n",
       "       [-0.79656381],\n",
       "       [-0.74390974],\n",
       "       [-0.75467989],\n",
       "       [-0.81092401],\n",
       "       [-0.81280451],\n",
       "       [-0.79006753]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_norm[:10]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation for LSTM training\n",
    "\n",
    "Here we'll create our list of (seq/label) tuples from the training set. Recall that an LSTM consumes a window of samples toward the first prediction, so the size of our training set will become ((325 - test_size) - window_size)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "301"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert train_norm from an array to a tensor\n",
    "train_norm = torch.FloatTensor(train_norm).view(-1)\n",
    "\n",
    "# Define a window size\n",
    "window_size = 12\n",
    "\n",
    "# Define function to create seq/label tuples\n",
    "def input_data(seq, ws):  # ws is the window size\n",
    "    out = []\n",
    "    L = len(seq)\n",
    "    for i in range(L-ws):\n",
    "        window = seq[i:i+ws]\n",
    "        label = seq[i+ws:i+ws+1]\n",
    "        out.append((window,label))\n",
    "    return out\n",
    "\n",
    "# Apply the input_data function to train_norm\n",
    "train_data = input_data(train_norm,window_size)\n",
    "len(train_data)  # this should equal 325-12-12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(tensor([-0.9268, -0.9270, -0.8340, -0.7379, -0.7966, -0.7439, -0.7547, -0.8109,\n",
       "          -0.8128, -0.7901, -0.7933, -0.6743]),\n",
       "  tensor([-1.])),\n",
       " (tensor([-0.9270, -0.8340, -0.7379, -0.7966, -0.7439, -0.7547, -0.8109, -0.8128,\n",
       "          -0.7901, -0.7933, -0.6743, -1.0000]),\n",
       "  tensor([-0.9607]))]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[:2]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the model\n",
    "\n",
    "We will be using an LSTM layer of size (1, 1000)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SalesPredictor(nn.Module):\n",
    "\n",
    "    def __init__(self, input_size=1, hidden_size=100, output_size=1):\n",
    "        super().__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        self.lstm = nn.LSTM(input_size, hidden_size)\n",
    "        self.linear = nn.Linear(hidden_size, output_size)\n",
    "        self.hidden = (torch.zeros(1, 1, self.hidden_size),\n",
    "                       torch.zeros(1, 1, self.hidden_size))\n",
    "    def forward(self, seq):\n",
    "        out, self.hidden = self.lstm(seq.view(len(seq), 1, -1), self.hidden)\n",
    "        pred = self.linear(out.view(len(seq), -1))\n",
    "        return pred[-1]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate the model and define the loss and optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalesPredictor(\n",
       "  (lstm): LSTM(1, 100)\n",
       "  (linear): Linear(in_features=100, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictorModel = SalesPredictor()\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(predictorModel.parameters(), lr=0.001)\n",
    "\n",
    "predictorModel"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model's training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  1 Loss: 0.35190338\n",
      "Epoch:  2 Loss: 0.26623961\n",
      "Epoch:  3 Loss: 0.36587891\n",
      "Epoch:  4 Loss: 0.33205000\n",
      "Epoch:  5 Loss: 0.33353198\n",
      "Epoch:  6 Loss: 0.32276595\n",
      "Epoch:  7 Loss: 0.32566583\n",
      "Epoch:  8 Loss: 0.30425122\n",
      "Epoch:  9 Loss: 0.32415488\n",
      "Epoch: 10 Loss: 0.28229472\n",
      "Epoch: 11 Loss: 0.31547359\n",
      "Epoch: 12 Loss: 0.27549812\n",
      "Epoch: 13 Loss: 0.29986337\n",
      "Epoch: 14 Loss: 0.25181815\n",
      "Epoch: 15 Loss: 0.28466749\n",
      "Epoch: 16 Loss: 0.20359342\n",
      "Epoch: 17 Loss: 0.16369571\n",
      "Epoch: 18 Loss: 0.08742281\n",
      "Epoch: 19 Loss: 0.00328820\n",
      "Epoch: 20 Loss: 0.17624366\n",
      "Epoch: 21 Loss: 0.04717605\n",
      "Epoch: 22 Loss: 0.00559546\n",
      "Epoch: 23 Loss: 0.01555043\n",
      "Epoch: 24 Loss: 0.02322063\n",
      "Epoch: 25 Loss: 0.06504883\n",
      "Epoch: 26 Loss: 0.04276101\n",
      "Epoch: 27 Loss: 0.00078148\n",
      "Epoch: 28 Loss: 0.03312317\n",
      "Epoch: 29 Loss: 0.04975151\n",
      "Epoch: 30 Loss: 0.03943659\n",
      "Epoch: 31 Loss: 0.01267170\n",
      "Epoch: 32 Loss: 0.00776969\n",
      "Epoch: 33 Loss: 0.00725949\n",
      "Epoch: 34 Loss: 0.00771704\n",
      "Epoch: 35 Loss: 0.00872793\n",
      "Epoch: 36 Loss: 0.00943791\n",
      "Epoch: 37 Loss: 0.00894835\n",
      "Epoch: 38 Loss: 0.00614477\n",
      "Epoch: 39 Loss: 0.00905585\n",
      "Epoch: 40 Loss: 0.02586778\n",
      "Epoch: 41 Loss: 0.01869943\n",
      "Epoch: 42 Loss: 0.01087153\n",
      "Epoch: 43 Loss: 0.00717327\n",
      "Epoch: 44 Loss: 0.00576406\n",
      "Epoch: 45 Loss: 0.00384349\n",
      "Epoch: 46 Loss: 0.00126416\n",
      "Epoch: 47 Loss: 0.00010236\n",
      "Epoch: 48 Loss: 0.00018587\n",
      "Epoch: 49 Loss: 0.00063291\n",
      "Epoch: 50 Loss: 0.00068379\n",
      "Epoch: 51 Loss: 0.00051581\n",
      "Epoch: 52 Loss: 0.00021699\n",
      "Epoch: 53 Loss: 0.00001485\n",
      "Epoch: 54 Loss: 0.00000940\n",
      "Epoch: 55 Loss: 0.00002078\n",
      "Epoch: 56 Loss: 0.00008459\n",
      "Epoch: 57 Loss: 0.00026093\n",
      "Epoch: 58 Loss: 0.00037156\n",
      "Epoch: 59 Loss: 0.00033317\n",
      "Epoch: 60 Loss: 0.00032976\n",
      "Epoch: 61 Loss: 0.00041958\n",
      "Epoch: 62 Loss: 0.00044047\n",
      "Epoch: 63 Loss: 0.00050251\n",
      "Epoch: 64 Loss: 0.00105786\n",
      "Epoch: 65 Loss: 0.00012933\n",
      "Epoch: 66 Loss: 0.00079503\n",
      "Epoch: 67 Loss: 0.00034435\n",
      "Epoch: 68 Loss: 0.00008737\n",
      "Epoch: 69 Loss: 0.00026838\n",
      "Epoch: 70 Loss: 0.00051029\n",
      "Epoch: 71 Loss: 0.00040797\n",
      "Epoch: 72 Loss: 0.00032314\n",
      "Epoch: 73 Loss: 0.00033714\n",
      "Epoch: 74 Loss: 0.00057400\n",
      "Epoch: 75 Loss: 0.00246433\n",
      "Epoch: 76 Loss: 0.00277913\n",
      "Epoch: 77 Loss: 0.00184956\n",
      "Epoch: 78 Loss: 0.00029109\n",
      "Epoch: 79 Loss: 0.00015206\n",
      "Epoch: 80 Loss: 0.00046554\n",
      "Epoch: 81 Loss: 0.00284721\n",
      "Epoch: 82 Loss: 0.00022324\n",
      "Epoch: 83 Loss: 0.00173459\n",
      "Epoch: 84 Loss: 0.00000126\n",
      "Epoch: 85 Loss: 0.00006724\n",
      "Epoch: 86 Loss: 0.00159185\n",
      "Epoch: 87 Loss: 0.00079963\n",
      "Epoch: 88 Loss: 0.00010014\n",
      "Epoch: 89 Loss: 0.00055208\n",
      "Epoch: 90 Loss: 0.00174644\n",
      "Epoch: 91 Loss: 0.00001737\n",
      "Epoch: 92 Loss: 0.00011067\n",
      "Epoch: 93 Loss: 0.00003381\n",
      "Epoch: 94 Loss: 0.00011891\n",
      "Epoch: 95 Loss: 0.00051561\n",
      "Epoch: 96 Loss: 0.00019498\n",
      "Epoch: 97 Loss: 0.00012084\n",
      "Epoch: 98 Loss: 0.00000491\n",
      "Epoch: 99 Loss: 0.00039270\n",
      "Epoch: 100 Loss: 0.00003053\n",
      "\n",
      "Duration: 37 seconds\n"
     ]
    }
   ],
   "source": [
    "epochs = 100\n",
    "\n",
    "import time\n",
    "start_time = time.time()\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    \n",
    "    # extract the sequence & label from the training data\n",
    "    for seq, y_train in train_data:\n",
    "        \n",
    "        # reset the parameters and hidden states\n",
    "        optimizer.zero_grad()\n",
    "        predictorModel.hidden = (torch.zeros(1,1,predictorModel.hidden_size),\n",
    "                        torch.zeros(1,1,predictorModel.hidden_size))\n",
    "        \n",
    "        y_pred = predictorModel(seq)\n",
    "        \n",
    "        loss = criterion(y_pred, y_train)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "    # print training result\n",
    "    print(f'Epoch: {epoch+1:2} Loss: {loss.item():10.8f}')\n",
    "    \n",
    "print(f'\\nDuration: {time.time() - start_time:.0f} seconds')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model's inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "future = 12\n",
    "\n",
    "# Add the last window of training values to the list of predictions\n",
    "preds = train_norm[-window_size:].tolist()\n",
    "\n",
    "# Set the model to evaluation mode\n",
    "predictorModel.eval()\n",
    "\n",
    "for i in range(future):\n",
    "    seq = torch.FloatTensor(preds[-window_size:])\n",
    "    with torch.no_grad():\n",
    "        predictorModel.hidden = (torch.zeros(1,1,predictorModel.hidden_size),\n",
    "                        torch.zeros(1,1,predictorModel.hidden_size))\n",
    "        preds.append(predictorModel(seq).item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.2915477454662323,\n",
       " 0.6417349576950073,\n",
       " 0.541263997554779,\n",
       " 0.7784364223480225,\n",
       " 0.9582229256629944,\n",
       " 0.42163175344467163,\n",
       " 0.719091534614563,\n",
       " 0.5792573690414429,\n",
       " 0.6042646169662476,\n",
       " 0.7744214534759521,\n",
       " 0.9033939838409424,\n",
       " 0.08176461607217789]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds[window_size:]  "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's invert the prediction so that we can compare them with original values in test sets.\n",
    "\n",
    "Note that <tt>inverse_transform</tt> uses the most recently applied parameters; we can rescale based on the test data, but not on the previous training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "true_prediction = scaler.inverse_transform(np.array(preds[window_size:]).reshape(1, -1))\n",
    "true_prediction = true_prediction.squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/Users/ajitkumarsingh/Desktop/Hands-on-with-PyTorch/Time Series Analysis with PyTorch/notebooks/Time-Series-Analysis-and-Predictions-RNN.ipynb Cell 36\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/ajitkumarsingh/Desktop/Hands-on-with-PyTorch/Time%20Series%20Analysis%20with%20PyTorch/notebooks/Time-Series-Analysis-and-Predictions-RNN.ipynb#X50sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m df[\u001b[39m'\u001b[39m\u001b[39mS4248SM144NCEN\u001b[39m\u001b[39m'\u001b[39m][\u001b[39m-\u001b[39m\u001b[39m12\u001b[39m:]\n",
      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "df['S4248SM144NCEN'][-12:]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the results\n",
    "Our original data contains a datetime index, but our predicted values do not. We can create a range of dates using NumPy that are spaced one month apart using <tt>dtype='datetime64[M]'</tt>, and then store them with day values to match our dataset with <tt>.astype('datetime64[D]')</tt>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2018-02-01', '2018-03-01', '2018-04-01', '2018-05-01',\n",
       "       '2018-06-01', '2018-07-01', '2018-08-01', '2018-09-01',\n",
       "       '2018-10-01', '2018-11-01', '2018-12-01', '2019-01-01'],\n",
       "      dtype='datetime64[D]')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remember that the stop date has to be later than the last predicted value.\n",
    "x = np.arange('2018-02-01', '2019-02-01', dtype='datetime64[M]').astype('datetime64[D]')\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "plt.title('Beer, Wine, and Alcohol Sales')\n",
    "plt.ylabel('Sales (millions of dollars)')\n",
    "plt.grid(True)\n",
    "plt.autoscale(axis='x',tight=True)\n",
    "plt.plot(df['S4248SM144NCEN'])\n",
    "plt.plot(x,true_prediction)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the end of the graph\n",
    "fig = plt.figure(figsize=(12,4))\n",
    "plt.title('Beer, Wine, and Alcohol Sales')\n",
    "plt.ylabel('Sales (millions of dollars)')\n",
    "plt.grid(True)\n",
    "plt.autoscale(axis='x',tight=True)\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "# Select the end of the graph with slice notation:\n",
    "plt.plot(df['S4248SM144NCEN']['2017-01-01':])\n",
    "plt.plot(x,true_prediction)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-0.9268, -0.9270, -0.8340, -0.7379, -0.7966, -0.7439, -0.7547, -0.8109,\n",
       "        -0.8128, -0.7901, -0.7933, -0.6743, -1.0000, -0.9607, -0.8070, -0.7699,\n",
       "        -0.7819, -0.7154, -0.7560, -0.7562, -0.7721, -0.7940, -0.7489, -0.6678,\n",
       "        -0.9925, -0.9408, -0.7586, -0.7897, -0.7557, -0.6660, -0.7790, -0.7114,\n",
       "        -0.7704, -0.7919, -0.7003, -0.6683, -0.9420, -0.9226, -0.7884, -0.8354,\n",
       "        -0.7121, -0.6678, -0.7962, -0.6634, -0.7942, -0.7396, -0.6889, -0.6931,\n",
       "        -0.8858, -0.8373, -0.7896, -0.7285, -0.6260, -0.6914, -0.6735, -0.6528,\n",
       "        -0.7719, -0.6613, -0.6890, -0.6468, -0.8757, -0.8643, -0.7567, -0.7258,\n",
       "        -0.6275, -0.6536, -0.6239, -0.6576, -0.6685, -0.6049, -0.6923, -0.4984,\n",
       "        -0.8692, -0.8311, -0.6808, -0.6480, -0.6229, -0.5870, -0.5890, -0.6451,\n",
       "        -0.5753, -0.5637, -0.6042, -0.4435, -0.8289, -0.7600, -0.5815, -0.5808,\n",
       "        -0.5630, -0.4623, -0.5572, -0.5114, -0.5218, -0.5182, -0.4617, -0.3606,\n",
       "        -0.7928, -0.6716, -0.4911, -0.6030, -0.4181, -0.3774, -0.5547, -0.3847,\n",
       "        -0.5118, -0.4374, -0.4035, -0.3765, -0.6494, -0.6265, -0.5107, -0.5106,\n",
       "        -0.3558, -0.3784, -0.4285, -0.3488, -0.5651, -0.3993, -0.3576, -0.2907,\n",
       "        -0.6434, -0.5878, -0.4348, -0.4027, -0.2846, -0.3623, -0.3287, -0.3095,\n",
       "        -0.4671, -0.3345, -0.3700, -0.1548, -0.6077, -0.5593, -0.4217, -0.3658,\n",
       "        -0.2969, -0.2987, -0.2244, -0.2760, -0.3840, -0.2145, -0.3483, -0.0582,\n",
       "        -0.5559, -0.5099, -0.3138, -0.3054, -0.2646, -0.1645, -0.2721, -0.2538,\n",
       "        -0.2772, -0.2757, -0.1965,  0.0303, -0.5682, -0.4519, -0.1979, -0.2767,\n",
       "        -0.1093, -0.0211, -0.2613, -0.0657, -0.1948, -0.2001, -0.1083,  0.0037,\n",
       "        -0.4812, -0.3916, -0.1637, -0.2642, -0.0064,  0.0763, -0.1861,  0.0471,\n",
       "        -0.1442, -0.0411,  0.0423,  0.1136, -0.3852, -0.3654, -0.1172, -0.1799,\n",
       "         0.1179,  0.1032, -0.0466,  0.1133, -0.1295,  0.1314,  0.0871,  0.2025,\n",
       "        -0.3056, -0.2389, -0.0881,  0.0025,  0.1562,  0.1875,  0.1150,  0.0845,\n",
       "         0.0589,  0.1825, -0.0209,  0.3348, -0.2760, -0.2227, -0.0329,  0.0481,\n",
       "         0.0835,  0.2199,  0.1071,  0.0073,  0.0669,  0.0333,  0.0117,  0.3355,\n",
       "        -0.3970, -0.2393,  0.1016,  0.0929,  0.0804,  0.2858,  0.0336,  0.0671,\n",
       "         0.0922,  0.0910,  0.1685,  0.4401, -0.3384, -0.1481,  0.1627,  0.0685,\n",
       "         0.1857,  0.4211,  0.0386,  0.2716,  0.2059,  0.1251,  0.2475,  0.4449,\n",
       "        -0.2384, -0.0409,  0.1416,  0.0927,  0.4208,  0.4092,  0.1649,  0.3902,\n",
       "         0.1114,  0.3203,  0.3543,  0.5200, -0.0830,  0.0013,  0.2102,  0.2757,\n",
       "         0.5706,  0.4282,  0.3941,  0.4413,  0.2758,  0.4547,  0.3859,  0.6545,\n",
       "        -0.0553,  0.0305,  0.2008,  0.3584,  0.4977,  0.5169,  0.3564,  0.3591,\n",
       "         0.3329,  0.5020,  0.2717,  0.7572, -0.0821,  0.0310,  0.3148,  0.3806,\n",
       "         0.4492,  0.6878,  0.5109,  0.4006,  0.5371,  0.5304,  0.4488,  0.9065,\n",
       "        -0.0397,  0.2463,  0.5516,  0.4341,  0.5740,  0.8207,  0.3538,  0.6530,\n",
       "         0.5798,  0.5171,  0.7070,  0.9472,  0.0278,  0.2687,  0.6155,  0.4463,\n",
       "         0.7974,  1.0000,  0.4335,  0.7730,  0.5174,  0.6548,  0.7489,  0.9205,\n",
       "         0.1168])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}