ma1249/RNN_TimeSeriesForcasting_Automated_UI

1{2 "cells": [3  {4   "cell_type": "code",5   "execution_count": 49,6   "metadata": {},7   "outputs": [],8   "source": [9    "import torch\n",10    "import torch.nn as nn\n",11    "import torch.optim as optim\n",12    "from torch.utils.data import Dataset, DataLoader\n",13    "import pandas as pd\n",14    "from sklearn.preprocessing import StandardScaler"15   ]16  },17  {18   "cell_type": "code",19   "execution_count": 50,20   "metadata": {},21   "outputs": [],22   "source": [23    "class TimeSeriesDataset(Dataset):\n",24    "    def __init__(self, data, seq_length=60, pred_length=7):\n",25    "        \"\"\"\n",26    "        data: The time series data (1D numpy array or 1D torch tensor)\n",27    "        seq_length: Number of lags (timesteps) to use as input\n",28    "        pred_length: Number of timesteps to predict (e.g., 7 for predicting the next 7 days)\n",29    "        \"\"\"\n",30    "        self.data = torch.tensor(data, dtype=torch.float32)\n",31    "        self.seq_length = seq_length\n",32    "        self.pred_length = pred_length\n",33    "    \n",34    "    def __len__(self):\n",35    "        # The length is reduced by the sequence length and prediction length\n",36    "        return len(self.data) - self.seq_length - self.pred_length + 1\n",37    "    \n",38    "    def __getitem__(self, index):\n",39    "        # Extract the sequence of 60 lags (the input)\n",40    "        x = self.data[index:index + self.seq_length]\n",41    "        # The target is the next 7 values (the output)\n",42    "        y = self.data[index + self.seq_length:index + self.seq_length + self.pred_length]\n",43    "        return x.unsqueeze(1), y  # Add feature dimension for input (sequence_length, 1)"44   ]45  },46  {47   "cell_type": "code",48   "execution_count": 51,49   "metadata": {},50   "outputs": [],51   "source": [52    "# Step 2: Define the RNN model\n",53    "class RNN(nn.Module):\n",54    "    def __init__(self, input_size, hidden_size, output_size, num_layers=1):\n",55    "        super(RNN, self).__init__()\n",56    "        \n",57    "        # RNN layer\n",58    "        self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True)\n",59    "        \n",60    "        # Fully connected output layer\n",61    "        self.fc = nn.Linear(hidden_size, output_size)\n",62    "    \n",63    "    def forward(self, x):\n",64    "        # Forward propagate the RNN\n",65    "        out, _ = self.rnn(x)  # Ignore the hidden state\n",66    "        \n",67    "        # Pass the output of the RNN to the fully connected layer\n",68    "        out = self.fc(out[:, -1, :])  # Only take the last output for each sequence\n",69    "        return out"70   ]71  },72  {73   "cell_type": "code",74   "execution_count": 52,75   "metadata": {},76   "outputs": [],77   "source": [78    "# Step 3: Set up hyperparameters\n",79    "input_size = 1  # Number of features in the input (1D time series)\n",80    "hidden_size = 32 * 3  # Number of hidden units\n",81    "output_size = 7  # Number of output units (regression or binary classification)\n",82    "num_layers = 4  # Number of RNN layers\n",83    "learning_rate = 0.0001\n",84    "num_epochs = 100\n",85    "batch_size = 128\n",86    "seq_length = 30 * 3"87   ]88  },89  {90   "cell_type": "code",91   "execution_count": 53,92   "metadata": {},93   "outputs": [94    {95     "name": "stdout",96     "output_type": "stream",97     "text": [98      "Using device: cuda\n"99     ]100    }101   ],102   "source": [103    "# Step 4: Check for GPU availability and set the device\n",104    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",105    "print(f'Using device: {device}')"106   ]107  },108  {109   "cell_type": "code",110   "execution_count": 54,111   "metadata": {},112   "outputs": [113    {114     "data": {115      "text/plain": [116       "array([-0.93666042, -0.94019707, -0.93859143, ...,  1.62146524,\n",117       "        1.63066054,  1.62111158])"118      ]119     },120     "execution_count": 54,121     "metadata": {},122     "output_type": "execute_result"123    }124   ],125   "source": [126    "# Step 5: Generate synthetic time series data (replace with real data)\n",127    "data = pd.read_csv('data.txt').Close.to_frame()\n",128    "scaler = StandardScaler()\n",129    "data = scaler.fit_transform(data)[:,0]\n",130    "data"131   ]132  },133  {134   "cell_type": "code",135   "execution_count": 55,136   "metadata": {},137   "outputs": [138    {139     "data": {140      "text/plain": [141       "(tensor([], size=(0, 1)),\n",142       " tensor([-0.9511, -0.9506, -0.9490, -0.9490, -0.9442, -0.9395, -0.9381]))"143      ]144     },145     "execution_count": 55,146     "metadata": {},147     "output_type": "execute_result"148    }149   ],150   "source": [151    "# Step 6: Create the dataset and DataLoader\n",152    "dataset = TimeSeriesDataset(data, seq_length=seq_length)\n",153    "dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)\n",154    "dataset[-1]"155   ]156  },157  {158   "cell_type": "code",159   "execution_count": 56,160   "metadata": {},161   "outputs": [],162   "source": [163    "# Step 7: Instantiate the model, loss function, and optimizer\n",164    "model = RNN(input_size, hidden_size, output_size, num_layers).to(device)  # Move model to GPU\n",165    "criterion = nn.L1Loss()\n",166    "optimizer = optim.Adam(model.parameters(), lr=learning_rate)"167   ]168  },169  {170   "cell_type": "code",171   "execution_count": 57,172   "metadata": {},173   "outputs": [174    {175     "name": "stdout",176     "output_type": "stream",177     "text": [178      "Epoch [10/100], Loss: 0.6917\n",179      "Epoch [20/100], Loss: 0.1477\n",180      "Epoch [30/100], Loss: 0.1170\n",181      "Epoch [40/100], Loss: 0.1201\n",182      "Epoch [50/100], Loss: 0.1233\n",183      "Epoch [60/100], Loss: 0.1203\n",184      "Epoch [70/100], Loss: 0.1143\n",185      "Epoch [80/100], Loss: 0.1060\n",186      "Epoch [90/100], Loss: 0.1058\n",187      "Epoch [100/100], Loss: 0.1009\n",188      "Training complete!\n"189     ]190    }191   ],192   "source": [193    "# Step 8: Training loop\n",194    "for epoch in range(num_epochs):\n",195    "    for i, (inputs, targets) in enumerate(dataloader):\n",196    "        # Move inputs and targets to GPU\n",197    "        inputs, targets = inputs.to(device), targets.to(device)\n",198    "        \n",199    "        # Zero the parameter gradients\n",200    "        optimizer.zero_grad()\n",201    "        \n",202    "        # Forward pass\n",203    "        outputs = model(inputs)\n",204    "        \n",205    "        # Calculate the loss\n",206    "        loss = criterion(outputs, targets)\n",207    "        \n",208    "        # Backward pass and optimize\n",209    "        loss.backward()\n",210    "        optimizer.step()\n",211    "    \n",212    "    # Print the loss every 10 epochs\n",213    "    if (epoch+1) % 10 == 0:\n",214    "        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')\n",215    "\n",216    "print(\"Training complete!\")"217   ]218  },219  {220   "cell_type": "code",221   "execution_count": 58,222   "metadata": {},223   "outputs": [224    {225     "data": {226      "text/plain": [227       "torch.Size([91, 90, 1])"228      ]229     },230     "execution_count": 58,231     "metadata": {},232     "output_type": "execute_result"233    }234   ],235   "source": [236    "inputs.shape"237   ]238  },239  {240   "cell_type": "code",241   "execution_count": 59,242   "metadata": {},243   "outputs": [244    {245     "data": {246      "text/plain": [247       "torch.Size([91, 7])"248      ]249     },250     "execution_count": 59,251     "metadata": {},252     "output_type": "execute_result"253    }254   ],255   "source": [256    "model(inputs.to(device)).shape"257   ]258  },259  {260   "cell_type": "code",261   "execution_count": 60,262   "metadata": {263    "scrolled": true264   },265   "outputs": [266    {267     "data": {268      "text/plain": [269       "array([[95.1     , 95.946   , 95.329   , 94.105   , 94.274   , 94.792   ,\n",270       "        92.094   ],\n",271       "       [95.946   , 95.329   , 94.105   , 94.274   , 94.792   , 92.094   ,\n",272       "        93.488   ],\n",273       "       [95.329   , 94.105   , 94.274   , 94.792   , 92.094   , 93.488   ,\n",274       "        92.074   ],\n",275       "       [94.105   , 94.274   , 94.792   , 92.094   , 93.488   , 92.074   ,\n",276       "        91.765   ],\n",277       "       [94.274   , 94.792   , 92.094   , 93.488   , 92.074   , 91.765   ,\n",278       "        92.791   ],\n",279       "       [94.792   , 92.094   , 93.488   , 92.074   , 91.765   , 92.791   ,\n",280       "        92.442   ],\n",281       "       [92.094   , 93.488   , 92.074   , 91.765   , 92.791   , 92.442   ,\n",282       "        92.542   ],\n",283       "       [93.488   , 92.074   , 91.765   , 92.791   , 92.442   , 92.542   ,\n",284       "        92.293   ],\n",285       "       [92.074   , 91.765   , 92.791   , 92.442   , 92.542   , 92.293   ,\n",286       "        90.959   ],\n",287       "       [91.765   , 92.791   , 92.442   , 92.542   , 92.293   , 90.959   ,\n",288       "        90.76    ],\n",289       "       [92.791   , 92.442   , 92.542   , 92.293   , 90.959   , 90.76    ,\n",290       "        91.535995],\n",291       "       [92.442   , 92.542   , 92.293   , 90.959   , 90.76    , 91.535995,\n",292       "        91.208   ],\n",293       "       [92.542   , 92.293   , 90.959   , 90.76    , 91.535995, 91.208   ,\n",294       "        92.711006],\n",295       "       [92.293   , 90.959   , 90.76    , 91.535995, 91.208   , 92.711006,\n",296       "        94.224   ],\n",297       "       [90.959   , 90.76    , 91.535995, 91.208   , 92.711006, 94.224   ,\n",298       "        93.786   ],\n",299       "       [90.76    , 91.535995, 91.208   , 92.711006, 94.224   , 93.786   ,\n",300       "        92.363   ],\n",301       "       [91.535995, 91.208   , 92.711006, 94.224   , 93.786   , 92.363   ,\n",302       "        93.547   ],\n",303       "       [91.208   , 92.711006, 94.224   , 93.786   , 92.363   , 93.547   ,\n",304       "        93.398   ],\n",305       "       [92.711006, 94.224   , 93.786   , 92.363   , 93.547   , 93.398   ,\n",306       "        92.562   ],\n",307       "       [94.224   , 93.786   , 92.363   , 93.547   , 93.398   , 92.562   ,\n",308       "        90.859   ],\n",309       "       [93.786   , 92.363   , 93.547   , 93.398   , 92.562   , 90.859   ,\n",310       "        81.342   ],\n",311       "       [92.363   , 93.547   , 93.398   , 92.562   , 90.859   , 81.342   ,\n",312       "        80.466   ],\n",313       "       [93.547   , 93.398   , 92.562   , 90.859   , 81.342   , 80.466   ,\n",314       "        80.218   ],\n",315       "       [93.398   , 92.562   , 90.859   , 81.342   , 80.466   , 80.218   ,\n",316       "        79.969   ],\n",317       "       [92.562   , 90.859   , 81.342   , 80.466   , 80.218   , 79.969   ,\n",318       "        79.441   ],\n",319       "       [90.859   , 81.342   , 80.466   , 80.218   , 79.969   , 79.441   ,\n",320       "        78.525   ],\n",321       "       [81.342   , 80.466   , 80.218   , 79.969   , 79.441   , 78.525   ,\n",322       "        79.66    ],\n",323       "       [80.466   , 80.218   , 79.969   , 79.441   , 78.525   , 79.66    ,\n",324       "        79.72    ],\n",325       "       [80.218   , 79.969   , 79.441   , 78.525   , 79.66    , 79.72    ,\n",326       "        79.64    ],\n",327       "       [79.969   , 79.441   , 78.525   , 79.66    , 79.72    , 79.64    ,\n",328       "        79.67    ],\n",329       "       [79.441   , 78.525   , 79.66    , 79.72    , 79.64    , 79.67    ,\n",330       "        79.94    ],\n",331       "       [78.525   , 79.66    , 79.72    , 79.64    , 79.67    , 79.94    ,\n",332       "        78.6     ],\n",333       "       [79.66    , 79.72    , 79.64    , 79.67    , 79.94    , 78.6     ,\n",334       "        78.04    ],\n",335       "       [79.72    , 79.64    , 79.67    , 79.94    , 78.6     , 78.04    ,\n",336       "        79.229996],\n",337       "       [79.64    , 79.67    , 79.94    , 78.6     , 78.04    , 79.229996,\n",338       "        78.14    ],\n",339       "       [79.67    , 79.94    , 78.6     , 78.04    , 79.229996, 78.14    ,\n",340       "        78.4     ],\n",341       "       [79.94    , 78.6     , 78.04    , 79.229996, 78.14    , 78.4     ,\n",342       "        78.1     ],\n",343       "       [78.6     , 78.04    , 79.229996, 78.14    , 78.4     , 78.1     ,\n",344       "        78.66    ],\n",345       "       [78.04    , 79.229996, 78.14    , 78.4     , 78.1     , 78.66    ,\n",346       "        78.56    ],\n",347       "       [79.229996, 78.14    , 78.4     , 78.1     , 78.66    , 78.56    ,\n",348       "        78.33    ],\n",349       "       [78.14    , 78.4     , 78.1     , 78.66    , 78.56    , 78.33    ,\n",350       "        80.25    ],\n",351       "       [78.4     , 78.1     , 78.66    , 78.56    , 78.33    , 80.25    ,\n",352       "        80.24    ],\n",353       "       [78.1     , 78.66    , 78.56    , 78.33    , 80.25    , 80.24    ,\n",354       "        79.69    ],\n",355       "       [78.66    , 78.56    , 78.33    , 80.25    , 80.24    , 79.69    ,\n",356       "        80.72    ],\n",357       "       [78.56    , 78.33    , 80.25    , 80.24    , 79.69    , 80.72    ,\n",358       "        82.560005],\n",359       "       [78.33    , 80.25    , 80.24    , 79.69    , 80.72    , 82.560005,\n",360       "        82.5     ],\n",361       "       [80.25    , 80.24    , 79.69    , 80.72    , 82.560005, 82.5     ,\n",362       "        84.39    ],\n",363       "       [80.24    , 79.69    , 80.72    , 82.560005, 82.5     , 84.39    ,\n",364       "        83.9     ],\n",365       "       [79.69    , 80.72    , 82.560005, 82.5     , 84.39    , 83.9     ,\n",366       "        84.32    ],\n",367       "       [80.72    , 82.560005, 82.5     , 84.39    , 83.9     , 84.32    ,\n",368       "        80.69    ],\n",369       "       [82.560005, 82.5     , 84.39    , 83.9     , 84.32    , 80.69    ,\n",370       "        81.6     ],\n",371       "       [82.5     , 84.39    , 83.9     , 84.32    , 80.69    , 81.6     ,\n",372       "        80.67    ],\n",373       "       [84.39    , 83.9     , 84.32    , 80.69    , 81.6     , 80.67    ,\n",374       "        79.28    ],\n",375       "       [83.9     , 84.32    , 80.69    , 81.6     , 80.67    , 79.28    ,\n",376       "        79.5     ],\n",377       "       [84.32    , 80.69    , 81.6     , 80.67    , 79.28    , 79.5     ,\n",378       "        80.38    ],\n",379       "       [80.69    , 81.6     , 80.67    , 79.28    , 79.5     , 80.38    ,\n",380       "        81.05    ],\n",381       "       [81.6     , 80.67    , 79.28    , 79.5     , 80.38    , 81.05    ,\n",382       "        80.979996],\n",383       "       [80.67    , 79.28    , 79.5     , 80.38    , 81.05    , 80.979996,\n",384       "        82.      ],\n",385       "       [79.28    , 79.5     , 80.38    , 81.05    , 80.979996, 82.      ,\n",386       "        82.43    ],\n",387       "       [79.5     , 80.38    , 81.05    , 80.979996, 82.      , 82.43    ,\n",388       "        80.99    ],\n",389       "       [80.38    , 81.05    , 80.979996, 82.      , 82.43    , 80.99    ,\n",390       "        82.75    ],\n",391       "       [81.05    , 80.979996, 82.      , 82.43    , 80.99    , 82.75    ,\n",392       "        83.3     ],\n",393       "       [80.979996, 82.      , 82.43    , 80.99    , 82.75    , 83.3     ,\n",394       "        82.88    ],\n",395       "       [82.      , 82.43    , 80.99    , 82.75    , 83.3     , 82.88    ,\n",396       "        82.74    ],\n",397       "       [82.43    , 80.99    , 82.75    , 83.3     , 82.88    , 82.74    ,\n",398       "        84.12    ],\n",399       "       [80.99    , 82.75    , 83.3     , 82.88    , 82.74    , 84.12    ,\n",400       "        80.479996],\n",401       "       [82.75    , 83.3     , 82.88    , 82.74    , 84.12    , 80.479996,\n",402       "        78.35    ],\n",403       "       [83.3     , 82.88    , 82.74    , 84.12    , 80.479996, 78.35    ,\n",404       "        79.55    ],\n",405       "       [82.88    , 82.74    , 84.12    , 80.479996, 78.35    , 79.55    ,\n",406       "        80.32    ],\n",407       "       [82.74    , 84.12    , 80.479996, 78.35    , 79.55    , 80.32    ,\n",408       "        80.8     ],\n",409       "       [84.12    , 80.479996, 78.35    , 79.55    , 80.32    , 80.8     ,\n",410       "        79.44    ],\n",411       "       [80.479996, 78.35    , 79.55    , 80.32    , 80.8     , 79.44    ,\n",412       "        79.      ],\n",413       "       [78.35    , 79.55    , 80.32    , 80.8     , 79.44    , 79.      ,\n",414       "        79.95    ],\n",415       "       [79.55    , 80.32    , 80.8     , 79.44    , 79.      , 79.95    ,\n",416       "        80.49    ],\n",417       "       [80.32    , 80.8     , 79.44    , 79.      , 79.95    , 80.49    ,\n",418       "        82.2     ],\n",419       "       [80.8     , 79.44    , 79.      , 79.95    , 80.49    , 82.2     ,\n",420       "        83.21    ],\n",421       "       [79.44    , 79.      , 79.95    , 80.49    , 82.2     , 83.21    ,\n",422       "        82.53    ],\n",423       "       [79.      , 79.95    , 80.49    , 82.2     , 83.21    , 82.53    ,\n",424       "        81.85    ],\n",425       "       [79.95    , 80.49    , 82.2     , 83.21    , 82.53    , 81.85    ,\n",426       "        82.22    ],\n",427       "       [80.49    , 82.2     , 83.21    , 82.53    , 81.85    , 82.22    ,\n",428       "        78.77    ],\n",429       "       [82.2     , 83.21    , 82.53    , 81.85    , 82.22    , 78.77    ,\n",430       "        76.380005],\n",431       "       [83.21    , 82.53    , 81.85    , 82.22    , 78.77    , 76.380005,\n",432       "        76.93    ],\n",433       "       [82.53    , 81.85    , 82.22    , 78.77    , 76.380005, 76.93    ,\n",434       "        76.95    ],\n",435       "       [81.85    , 82.22    , 78.77    , 76.380005, 76.93    , 76.95    ,\n",436       "        76.62    ],\n",437       "       [82.22    , 78.77    , 76.380005, 76.93    , 76.95    , 76.62    ,\n",438       "        73.23    ],\n",439       "       [78.77    , 76.380005, 76.93    , 76.95    , 76.62    , 73.23    ,\n",440       "        74.799995],\n",441       "       [76.380005, 76.93    , 76.95    , 76.62    , 73.23    , 74.799995,\n",442       "        74.86    ],\n",443       "       [76.93    , 76.95    , 76.62    , 73.23    , 74.799995, 74.86    ,\n",444       "        74.91    ],\n",445       "       [76.95    , 76.62    , 73.23    , 74.799995, 74.86    , 74.91    ,\n",446       "        75.01    ],\n",447       "       [76.62    , 73.23    , 74.799995, 74.86    , 74.91    , 75.01    ,\n",448       "        75.27    ],\n",449       "       [73.23    , 74.799995, 74.86    , 74.91    , 75.01    , 75.27    ,\n",450       "        75.      ]], dtype=float32)"451      ]452     },453     "execution_count": 60,454     "metadata": {},455     "output_type": "execute_result"456    }457   ],458   "source": [459    "scaler.inverse_transform(targets.cpu().numpy())"460   ]461  },462  {463   "cell_type": "code",464   "execution_count": 61,465   "metadata": {466    "scrolled": true467   },468   "outputs": [469    {470     "data": {471      "text/plain": [472       "array([[92.06912 , 92.27934 , 92.45875 , 92.60131 , 92.27289 , 92.23518 ,\n",473       "        92.47526 ],\n",474       "       [91.89131 , 92.0555  , 92.25016 , 92.391136, 92.05249 , 92.04596 ,\n",475       "        92.29005 ],\n",476       "       [91.87636 , 92.05091 , 92.22472 , 92.401535, 92.03708 , 92.048195,\n",477       "        92.29575 ],\n",478       "       [91.84402 , 92.029236, 92.2171  , 92.36785 , 92.008   , 92.012375,\n",479       "        92.25569 ],\n",480       "       [91.49964 , 91.62743 , 91.8567  , 91.97599 , 91.64492 , 91.64085 ,\n",481       "        91.88221 ],\n",482       "       [91.17312 , 91.25997 , 91.48723 , 91.65082 , 91.28838 , 91.32571 ,\n",483       "        91.56282 ],\n",484       "       [91.153755, 91.273346, 91.47779 , 91.68099 , 91.27869 , 91.343376,\n",485       "        91.5698  ],\n",486       "       [90.639496, 90.70645 , 90.99209 , 91.10308 , 90.762436, 90.794655,\n",487       "        91.01152 ],\n",488       "       [90.3554  , 90.39404 , 90.6389  , 90.84815 , 90.45934 , 90.538315,\n",489       "        90.75247 ],\n",490       "       [90.05984 , 90.09707 , 90.37753 , 90.56152 , 90.15287 , 90.26089 ,\n",491       "        90.45137 ],\n",492       "       [89.72495 , 89.74521 , 90.03026 , 90.22538 , 89.833305, 89.930595,\n",493       "        90.11761 ],\n",494       "       [89.77841 , 89.83489 , 90.083565, 90.33616 , 89.896255, 90.022835,\n",495       "        90.201126],\n",496       "       [89.9516  , 90.05013 , 90.299095, 90.52881 , 90.09728 , 90.20386 ,\n",497       "        90.37378 ],\n",498       "       [90.06678 , 90.16862 , 90.419014, 90.63228 , 90.2323  , 90.305954,\n",499       "        90.48347 ],\n",500       "       [90.05273 , 90.13475 , 90.39309 , 90.59782 , 90.20177 , 90.28201 ,\n",501       "        90.45954 ],\n",502       "       [89.64318 , 89.66102 , 89.96755 , 90.13687 , 89.76345 , 89.85026 ,\n",503       "        90.02426 ],\n",504       "       [89.1405  , 89.10798 , 89.42067 , 89.62576 , 89.22856 , 89.35743 ,\n",505       "        89.5257  ],\n",506       "       [89.04963 , 89.05395 , 89.33206 , 89.60128 , 89.14351 , 89.3158  ,\n",507       "        89.46981 ],\n",508       "       [89.13832 , 89.19185 , 89.47086 , 89.71934 , 89.27024 , 89.41663 ,\n",509       "        89.56306 ],\n",510       "       [89.606064, 89.71885 , 89.94736 , 90.23135 , 89.77745 , 89.898384,\n",511       "        90.052376],\n",512       "       [90.45306 , 90.65572 , 90.837265, 91.12348 , 90.66584 , 90.75453 ,\n",513       "        90.917885],\n",514       "       [90.9878  , 91.20914 , 91.410805, 91.60883 , 91.22548 , 91.241295,\n",515       "        91.423325],\n",516       "       [90.7574  , 90.87427 , 91.13702 , 91.264   , 90.94549 , 90.94166 ,\n",517       "        91.14112 ],\n",518       "       [90.56259 , 90.63026 , 90.865   , 91.07292 , 90.692535, 90.76116 ,\n",519       "        90.96616 ],\n",520       "       [90.52974 , 90.62691 , 90.86087 , 91.07173 , 90.66027 , 90.751686,\n",521       "        90.94904 ],\n",522       "       [90.35239 , 90.44133 , 90.6998  , 90.8744  , 90.49695 , 90.557816,\n",523       "        90.75553 ],\n",524       "       [89.72487 , 89.72067 , 90.04034 , 90.181564, 89.82455 , 89.89999 ,\n",525       "        90.08876 ],\n",526       "       [86.48407 , 86.04535 , 86.71416 , 86.58719 , 86.38407 , 86.50668 ,\n",527       "        86.642426],\n",528       "       [82.41076 , 81.63757 , 82.339745, 82.48406 , 82.099556, 82.543976,\n",529       "        82.595314],\n",530       "       [79.77171 , 79.18222 , 79.75498 , 80.2161  , 79.457756, 80.25006 ,\n",531       "        80.14603 ],\n",532       "       [79.15316 , 78.96858 , 79.383415, 79.9574  , 79.12178 , 79.86571 ,\n",533       "        79.704506],\n",534       "       [79.641075, 79.64512 , 80.00634 , 80.53714 , 79.825035, 80.37007 ,\n",535       "        80.204346],\n",536       "       [79.96584 , 79.92305 , 80.31219 , 80.76779 , 80.20546 , 80.64999 ,\n",537       "        80.46418 ],\n",538       "       [80.28538 , 80.21587 , 80.575264, 81.0751  , 80.54504 , 80.99284 ,\n",539       "        80.81003 ],\n",540       "       [80.61234 , 80.589905, 80.95039 , 81.41382 , 80.895065, 81.34877 ,\n",541       "        81.16425 ],\n",542       "       [80.774826, 80.75834 , 81.143036, 81.56098 , 81.07248 , 81.49198 ,\n",543       "        81.31227 ],\n",544       "       [80.80376 , 80.75117 , 81.14895 , 81.56632 , 81.06432 , 81.49822 ,\n",545       "        81.31802 ],\n",546       "       [80.85851 , 80.79213 , 81.18406 , 81.62587 , 81.09572 , 81.55568 ,\n",547       "        81.37619 ],\n",548       "       [80.4381  , 80.32942 , 80.781876, 81.15566 , 80.658905, 81.11045 ,\n",549       "        80.92539 ],\n",550       "       [79.66641 , 79.497505, 79.96495 , 80.359024, 79.852646, 80.34178 ,\n",551       "        80.14604 ],\n",552       "       [79.52979 , 79.41818 , 79.803764, 80.325775, 79.71688 , 80.27605 ,\n",553       "        80.07248 ],\n",554       "       [79.519844, 79.47473 , 79.87958 , 80.332054, 79.75672 , 80.27798 ,\n",555       "        80.06359 ],\n",556       "       [79.518745, 79.46702 , 79.85829 , 80.31992 , 79.78754 , 80.2623  ,\n",557       "        80.05792 ],\n",558       "       [79.44085 , 79.36616 , 79.77297 , 80.224174, 79.69011 , 80.1844  ,\n",559       "        79.96849 ],\n",560       "       [79.54565 , 79.49432 , 79.869316, 80.3575  , 79.80845 , 80.31023 ,\n",561       "        80.09834 ],\n",562       "       [79.709236, 79.68146 , 80.063705, 80.52524 , 79.989426, 80.47343 ,\n",563       "        80.26305 ],\n",564       "       [79.71878 , 79.67426 , 80.072266, 80.50991 , 79.99869 , 80.46565 ,\n",565       "        80.2558  ],\n",566       "       [80.316956, 80.31446 , 80.63799 , 81.17424 , 80.60424 , 81.086975,\n",567       "        80.89443 ],\n",568       "       [81.07588 , 81.123566, 81.461624, 81.93067 , 81.38966 , 81.82774 ,\n",569       "        81.64439 ],\n",570       "       [81.27427 , 81.25987 , 81.65894 , 82.0389  , 81.58498 , 81.955536,\n",571       "        81.78782 ],\n",572       "       [81.406685, 81.33915 , 81.721855, 82.15914 , 81.6622  , 82.077156,\n",573       "        81.91928 ],\n",574       "       [82.196396, 82.208855, 82.520645, 83.03385 , 82.457924, 82.90221 ,\n",575       "        82.75924 ],\n",576       "       [83.00996 , 83.074425, 83.410416, 83.83077 , 83.31484 , 83.67184 ,\n",577       "        83.5512  ],\n",578       "       [83.97628 , 84.042656, 84.34311 , 84.784805, 84.28563 , 84.59248 ,\n",579       "        84.508514],\n",580       "       [84.50985 , 84.53233 , 84.89264 , 85.24137 , 84.77833 , 85.063354,\n",581       "        84.991974],\n",582       "       [84.65385 , 84.6165  , 84.98795 , 85.331024, 84.88377 , 85.16388 ,\n",583       "        85.11771 ],\n",584       "       [83.437126, 83.2247  , 83.76016 , 83.949005, 83.56305 , 83.872246,\n",585       "        83.80549 ],\n",586       "       [82.18266 , 81.88778 , 82.36742 , 82.734566, 82.22968 , 82.67543 ,\n",587       "        82.59299 ],\n",588       "       [81.36292 , 81.138176, 81.60571 , 82.018326, 81.40552 , 81.949524,\n",589       "        81.81976 ],\n",590       "       [80.59396 , 80.4196  , 80.888885, 81.28098 , 80.70462 , 81.21209 ,\n",591       "        81.06117 ],\n",592       "       [80.15921 , 80.00237 , 80.43423 , 80.89312 , 80.29897 , 80.813995,\n",593       "        80.648544],\n",594       "       [80.473236, 80.40863 , 80.76194 , 81.29701 , 80.66128 , 81.1908  ,\n",595       "        81.01603 ],\n",596       "       [81.27432 , 81.305664, 81.63016 , 82.13748 , 81.55306 , 82.001114,\n",597       "        81.8408  ],\n",598       "       [81.895515, 81.92347 , 82.27563 , 82.701775, 82.20435 , 82.573265,\n",599       "        82.42702 ],\n",600       "       [82.43142 , 82.42524 , 82.77531 , 83.20856 , 82.71847 , 83.08139 ,\n",601       "        82.949905],\n",602       "       [82.904144, 82.89588 , 83.25214 , 83.668365, 83.16802 , 83.53691 ,\n",603       "        83.417534],\n",604       "       [82.66293 , 82.5832  , 83.02127 , 83.3376  , 82.8937  , 83.24095 ,\n",605       "        83.12418 ],\n",606       "       [82.67159 , 82.575325, 82.950874, 83.388985, 82.866   , 83.26831 ,\n",607       "        83.16506 ],\n",608       "       [83.16705 , 83.14737 , 83.49469 , 83.946434, 83.37078 , 83.79081 ,\n",609       "        83.686516],\n",610       "       [83.511795, 83.50626 , 83.87947 , 84.2566  , 83.75372 , 84.09249 ,\n",611       "        84.00499 ],\n",612       "       [83.49789 , 83.42462 , 83.82772 , 84.18038 , 83.708305, 84.03507 ,\n",613       "        83.95649 ],\n",614       "       [83.77519 , 83.711784, 84.06499 , 84.496414, 83.96124 , 84.335815,\n",615       "        84.263435],\n",616       "       [82.98652 , 82.83564 , 83.3396  , 83.577736, 83.14178 , 83.485405,\n",617       "        83.39301 ],\n",618       "       [81.000565, 80.64837 , 81.23718 , 81.470604, 81.05929 , 81.47061 ,\n",619       "        81.35197 ],\n",620       "       [79.69368 , 79.36554 , 79.8428  , 80.327095, 79.69179 , 80.31455 ,\n",621       "        80.14627 ],\n",622       "       [79.86685 , 79.790886, 80.14036 , 80.72912 , 79.99072 , 80.63175 ,\n",623       "        80.44375 ],\n",624       "       [80.87259 , 80.95677 , 81.26176 , 81.79581 , 81.16282 , 81.631676,\n",625       "        81.4686  ],\n",626       "       [81.2474  , 81.249115, 81.64646 , 82.02566 , 81.55106 , 81.90891 ,\n",627       "        81.745346],\n",628       "       [80.8024  , 80.65397 , 81.10636 , 81.470406, 81.03073 , 81.42665 ,\n",629       "        81.25664 ],\n",630       "       [80.53127 , 80.39148 , 80.7945  , 81.260666, 80.72111 , 81.22    ,\n",631       "        81.04374 ],\n",632       "       [80.81987 , 80.79367 , 81.15187 , 81.64315 , 81.05753 , 81.55739 ,\n",633       "        81.38195 ],\n",634       "       [81.86205 , 81.9379  , 82.23011 , 82.753334, 82.1672  , 82.59523 ,\n",635       "        82.44695 ],\n",636       "       [83.16397 , 83.275604, 83.563736, 84.034256, 83.498024, 83.84516 ,\n",637       "        83.7224  ],\n",638       "       [83.71355 , 83.743385, 84.11331 , 84.45604 , 84.02535 , 84.29913 ,\n",639       "        84.19975 ],\n",640       "       [83.34613 , 83.2322  , 83.67242 , 83.971794, 83.56836 , 83.86935 ,\n",641       "        83.78191 ],\n",642       "       [82.848854, 82.68501 , 83.11448 , 83.486595, 82.99457 , 83.39683 ,\n",643       "        83.30325 ],\n",644       "       [81.44063 , 81.192444, 81.74669 , 81.996895, 81.53275 , 81.969406,\n",645       "        81.83729 ],\n",646       "       [79.07527 , 78.68002 , 79.300095, 79.5679  , 79.09215 , 79.61783 ,\n",647       "        79.43858 ],\n",648       "       [77.41153 , 77.07292 , 77.5718  , 78.08546 , 77.40297 , 78.11898 ,\n",649       "        77.8832  ],\n",650       "       [77.1089  , 77.016716, 77.39133 , 78.00246 , 77.2374  , 77.96693 ,\n",651       "        77.70689 ],\n",652       "       [77.50964 , 77.561554, 77.89498 , 78.453766, 77.80486 , 78.37931 ,\n",653       "        78.1332  ]], dtype=float32)"654      ]655     },656     "execution_count": 61,657     "metadata": {},658     "output_type": "execute_result"659    }660   ],661   "source": [662    "scaler.inverse_transform(model(inputs).cpu().detach().numpy())"663   ]664  },665  {666   "cell_type": "code",667   "execution_count": 62,668   "metadata": {},669   "outputs": [670    {671     "data": {672      "text/plain": [673       "torch.Size([91, 7])"674      ]675     },676     "execution_count": 62,677     "metadata": {},678     "output_type": "execute_result"679    }680   ],681   "source": [682    "targets.shape"683   ]684  },685  {686   "cell_type": "code",687   "execution_count": 63,688   "metadata": {},689   "outputs": [690    {691     "data": {692      "image/png": "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",693      "text/plain": [694       "<Figure size 1200x600 with 1 Axes>"695      ]696     },697     "metadata": {},698     "output_type": "display_data"699    }700   ],701   "source": [702    "import matplotlib.pyplot as plt\n",703    "import torch\n",704    "import numpy as np\n",705    "\n",706    "# Step 1: Prepare the data for testing\n",707    "def predict_on_full_data(model, data, seq_length=60, pred_length=7, device='cpu'):\n",708    "    model.eval()  # Set the model to evaluation mode\n",709    "    predictions = []\n",710    "    \n",711    "    # Make sure the model and data are on the same device\n",712    "    model = model.to(device)\n",713    "    data = torch.tensor(data, dtype=torch.float32).to(device)\n",714    "    \n",715    "    with torch.no_grad():  # Disable gradient calculation for inference\n",716    "        for i in range(len(data) - seq_length - pred_length + 1):\n",717    "            # Get the input sequence (past 60 timesteps)\n",718    "            input_seq = data[i:i + seq_length].unsqueeze(1).unsqueeze(0)  # (1, seq_length, 1)\n",719    "            # Predict the next 7 timesteps\n",720    "            pred_seq = model(input_seq).squeeze().cpu().numpy()  # Move prediction to CPU\n",721    "            \n",722    "            predictions.append([pred_seq[0]])\n",723    "    \n",724    "    # Concatenate predictions to form a continuous output\n",725    "    predictions = np.concatenate(predictions)\n",726    "    \n",727    "    return predictions\n",728    "\n",729    "# Step 2: Get the actual data (excluding the initial lags)\n",730    "def get_actual_values(data, seq_length=60, pred_length=7):\n",731    "    return data[seq_length:len(data) - pred_length + 1]\n",732    "\n",733    "# Step 3: Use the model to predict on the entire dataset\n",734    "\n",735    "\n",736    "def plot_full_data_predictions():\n",737    "\n",738    "    predictions = predict_on_full_data(model, data, seq_length=seq_length, pred_length=7, device=device)\n",739    "\n",740    "    actual_values = get_actual_values(data, seq_length=seq_length, pred_length=7)\n",741    "\n",742    "    plt.figure(figsize=(12, 6))\n",743    "    plt.plot(range(len(actual_values)), actual_values, label='Actual Values', color='blue')\n",744    "    plt.plot(range(len(predictions)), predictions, label='Predicted Values', color='red', linestyle='--')\n",745    "    plt.title('Actual vs. Predicted Values')\n",746    "    plt.xlabel('Time Steps')\n",747    "    plt.ylabel('Values')\n",748    "    plt.legend()\n",749    "    plt.show()"750   ]751  },752  {753   "cell_type": "code",754   "execution_count": 64,755   "metadata": {},756   "outputs": [757    {758     "data": {759      "text/plain": [760       "array([[32.01568569]])"761      ]762     },763     "execution_count": 64,764     "metadata": {},765     "output_type": "execute_result"766    }767   ],768   "source": [769    "scaler.inverse_transform([[loss.item()]])"770   ]771  },772  {773   "cell_type": "code",774   "execution_count": 65,775   "metadata": {},776   "outputs": [777    {778     "name": "stdout",779     "output_type": "stream",780     "text": [781      "Epoch [10/100], Loss: 0.6504\n",782      "Epoch [20/100], Loss: 0.1760\n",783      "Epoch [30/100], Loss: 0.1802\n",784      "Epoch [40/100], Loss: 0.1506\n",785      "Epoch [50/100], Loss: 0.1381\n",786      "Epoch [60/100], Loss: 0.1309\n",787      "Epoch [70/100], Loss: 0.1266\n",788      "Epoch [80/100], Loss: 0.1249\n",789      "Epoch [90/100], Loss: 0.1230\n",790      "Epoch [100/100], Loss: 0.1229\n",791      "Training complete!\n"792     ]793    },794    {795     "data": {796      "image/png": "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",797      "text/plain": [798       "<Figure size 1200x600 with 1 Axes>"799      ]800     },801     "metadata": {},802     "output_type": "display_data"803    }804   ],805   "source": [806    "# Step 2: Define the RNN model\n",807    "class LSTM(nn.Module):\n",808    "    def __init__(self, input_size, hidden_size, output_size, num_layers=1):\n",809    "        super(LSTM, self).__init__()\n",810    "        \n",811    "        # RNN layer\n",812    "        self.rnn = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)\n",813    "        \n",814    "        # Fully connected output layer\n",815    "        self.fc = nn.Linear(hidden_size, output_size)\n",816    "    \n",817    "    def forward(self, x):\n",818    "        # Forward propagate the RNN\n",819    "        out, _ = self.rnn(x)  # Ignore the hidden state\n",820    "        \n",821    "        # Pass the output of the RNN to the fully connected layer\n",822    "        out = self.fc(out[:, -1, :])  # Only take the last output for each sequence\n",823    "        return out\n",824    "\n",825    "\n",826    "# Step 7: Instantiate the model, loss function, and optimizer\n",827    "model = LSTM(input_size, hidden_size, output_size, num_layers).to(device)  # Move model to GPU\n",828    "criterion = nn.L1Loss()\n",829    "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",830    "\n",831    "\n",832    "# Step 8: Training loop\n",833    "for epoch in range(num_epochs):\n",834    "    for i, (inputs, targets) in enumerate(dataloader):\n",835    "        # Move inputs and targets to GPU\n",836    "        inputs, targets = inputs.to(device), targets.to(device)\n",837    "        \n",838    "        # Zero the parameter gradients\n",839    "        optimizer.zero_grad()\n",840    "        \n",841    "        # Forward pass\n",842    "        outputs = model(inputs)\n",843    "        \n",844    "        # Calculate the loss\n",845    "        loss = criterion(outputs, targets)\n",846    "        \n",847    "        # Backward pass and optimize\n",848    "        loss.backward()\n",849    "        optimizer.step()\n",850    "    \n",851    "    # Print the loss every 10 epochs\n",852    "    if (epoch+1) % 10 == 0:\n",853    "        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')\n",854    "\n",855    "print(\"Training complete!\")\n",856    "\n",857    "\n",858    "# Step 3: Use the model to predict on the entire dataset\n",859    "predictions = predict_on_full_data(model, data, seq_length=seq_length, pred_length=7, device=device)\n",860    "\n",861    "# Get the actual target values for comparison\n",862    "actual_values = get_actual_values(data, seq_length=seq_length, pred_length=7)\n",863    "\n",864    "# Step 4: Plot the results\n",865    "plt.figure(figsize=(12, 6))\n",866    "plt.plot(range(len(actual_values)), actual_values, label='Actual Values', color='blue')\n",867    "plt.plot(range(len(predictions)), predictions, label='Predicted Values', color='red', linestyle='--')\n",868    "plt.title('Actual vs. Predicted Values')\n",869    "plt.xlabel('Time Steps')\n",870    "plt.ylabel('Values')\n",871    "plt.legend()\n",872    "plt.show()"873   ]874  },875  {876   "cell_type": "code",877   "execution_count": 66,878   "metadata": {},879   "outputs": [880    {881     "name": "stdout",882     "output_type": "stream",883     "text": [884      "Epoch [10/100], Loss: 0.2448\n",885      "Epoch [20/100], Loss: 0.1386\n",886      "Epoch [30/100], Loss: 0.1288\n",887      "Epoch [40/100], Loss: 0.1212\n",888      "Epoch [50/100], Loss: 0.1150\n",889      "Epoch [60/100], Loss: 0.1107\n",890      "Epoch [70/100], Loss: 0.1075\n",891      "Epoch [80/100], Loss: 0.1050\n",892      "Epoch [90/100], Loss: 0.1029\n",893      "Epoch [100/100], Loss: 0.1007\n",894      "Training complete!\n"895     ]896    },897    {898     "data": {899      "image/png": "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",900      "text/plain": [901       "<Figure size 1200x600 with 1 Axes>"902      ]903     },904     "metadata": {},905     "output_type": "display_data"906    }907   ],908   "source": [909    "# Step 2: Define the RNN model\n",910    "class GRU(nn.Module):\n",911    "    def __init__(self, input_size, hidden_size, output_size, num_layers=1):\n",912    "        super(GRU, self).__init__()\n",913    "        \n",914    "        # RNN layer\n",915    "        self.rnn = nn.GRU(input_size, hidden_size, num_layers, batch_first=True)\n",916    "        \n",917    "        # Fully connected output layer\n",918    "        self.fc = nn.Linear(hidden_size, output_size)\n",919    "    \n",920    "    def forward(self, x):\n",921    "        # Forward propagate the RNN\n",922    "        out, _ = self.rnn(x)  # Ignore the hidden state\n",923    "        \n",924    "        # Pass the output of the RNN to the fully connected layer\n",925    "        out = self.fc(out[:, -1, :])  # Only take the last output for each sequence\n",926    "        return out\n",927    "\n",928    "\n",929    "# Step 7: Instantiate the model, loss function, and optimizer\n",930    "model = GRU(input_size, hidden_size, output_size, num_layers).to(device)  # Move model to GPU\n",931    "criterion = nn.L1Loss()\n",932    "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",933    "\n",934    "\n",935    "# Step 8: Training loop\n",936    "for epoch in range(num_epochs):\n",937    "    for i, (inputs, targets) in enumerate(dataloader):\n",938    "        # Move inputs and targets to GPU\n",939    "        inputs, targets = inputs.to(device), targets.to(device)\n",940    "        \n",941    "        # Zero the parameter gradients\n",942    "        optimizer.zero_grad()\n",943    "        \n",944    "        # Forward pass\n",945    "        outputs = model(inputs)\n",946    "        \n",947    "        # Calculate the loss\n",948    "        loss = criterion(outputs, targets)\n",949    "        \n",950    "        # Backward pass and optimize\n",951    "        loss.backward()\n",952    "        optimizer.step()\n",953    "    \n",954    "    # Print the loss every 10 epochs\n",955    "    if (epoch+1) % 10 == 0:\n",956    "        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')\n",957    "\n",958    "print(\"Training complete!\")\n",959    "\n",960    "\n",961    "# Step 3: Use the model to predict on the entire dataset\n",962    "predictions = predict_on_full_data(model, data, seq_length=seq_length, pred_length=7, device=device)\n",963    "\n",964    "# Get the actual target values for comparison\n",965    "actual_values = get_actual_values(data, seq_length=seq_length, pred_length=7)\n",966    "\n",967    "# Step 4: Plot the results\n",968    "plt.figure(figsize=(12, 6))\n",969    "plt.plot(range(len(actual_values)), actual_values, label='Actual Values', color='blue')\n",970    "plt.plot(range(len(predictions)), predictions, label='Predicted Values', color='red', linestyle='--')\n",971    "plt.title('Actual vs. Predicted Values')\n",972    "plt.xlabel('Time Steps')\n",973    "plt.ylabel('Values')\n",974    "plt.legend()\n",975    "plt.show()"976   ]977  },978  {979   "cell_type": "code",980   "execution_count": null,981   "metadata": {},982   "outputs": [],983   "source": []984  }985 ],986 "metadata": {987  "kernelspec": {988   "display_name": "Python 3 (ipykernel)",989   "language": "python",990   "name": "python3"991  },992  "language_info": {993   "codemirror_mode": {994    "name": "ipython",995    "version": 3996   },997   "file_extension": ".py",998   "mimetype": "text/x-python",999   "name": "python",1000   "nbconvert_exporter": "python",1001   "pygments_lexer": "ipython3",1002   "version": "3.9.4"1003  }1004 },1005 "nbformat": 4,1006 "nbformat_minor": 41007}1008