global-equity-forecasting-using-lstm.ipynb

{
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: torchinfo in /opt/conda/lib/python3.10/site-packages (1.8.0)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install torchinfo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05c0bcef",
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     "start_time": "2024-12-16T03:13:11.742235",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Importing the relevant libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "248b3d94",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "import torch, warnings, time\n",
    "warnings.filterwarnings('ignore')\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error, mean_squared_error, mean_squared_log_error\n",
    "from torchinfo import summary\n",
    "from torch.nn.utils import clip_grad_norm_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f808e569",
   "metadata": {
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     "start_time": "2024-12-16T03:13:17.115733",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Loading the Global Equity dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "625fb095",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999-11-12</th>\n",
       "      <td>89.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-11-19</th>\n",
       "      <td>86.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-11-26</th>\n",
       "      <td>91.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-12-03</th>\n",
       "      <td>96.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-12-10</th>\n",
       "      <td>93.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            close\n",
       "1999-11-12  89.19\n",
       "1999-11-19  86.00\n",
       "1999-11-26  91.12\n",
       "1999-12-03  96.12\n",
       "1999-12-10  93.87"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/kaggle/input/global-equity-dataset/Global Equity.csv',index_col='Unnamed: 0',usecols=['Unnamed: 0','close'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cf733bb",
   "metadata": {
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     "start_time": "2024-12-16T03:13:17.202787",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eac381b5",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1108, 1)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6bdb011d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:17.265320Z",
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     "start_time": "2024-12-16T03:13:17.252949",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1108 entries, 1999-11-12 to 2021-01-29\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   close   1108 non-null   float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 17.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8445640e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:17.312042Z",
     "iopub.status.busy": "2024-12-16T03:13:17.311692Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.index.freq = 'W'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3472fbdc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:17.341028Z",
     "iopub.status.busy": "2024-12-16T03:13:17.340265Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['1999-11-12', '1999-11-19', '1999-11-26', '1999-12-03', '1999-12-10',\n",
       "       '1999-12-17', '1999-12-23', '1999-12-31', '2000-01-07', '2000-01-14',\n",
       "       ...\n",
       "       '2020-11-27', '2020-12-04', '2020-12-11', '2020-12-18', '2020-12-24',\n",
       "       '2020-12-31', '2021-01-08', '2021-01-15', '2021-01-22', '2021-01-29'],\n",
       "      dtype='object', length=1108)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fc293c0c",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(figsize=(10,5),title='Global Equity');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9aae521e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:17.722234Z",
     "iopub.status.busy": "2024-12-16T03:13:17.721856Z",
     "iopub.status.idle": "2024-12-16T03:13:17.729498Z",
     "shell.execute_reply": "2024-12-16T03:13:17.728579Z"
    },
    "papermill": {
     "duration": 0.021953,
     "end_time": "2024-12-16T03:13:17.731571",
     "exception": false,
     "start_time": "2024-12-16T03:13:17.709618",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "close    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c783a888",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:17.756075Z",
     "iopub.status.busy": "2024-12-16T03:13:17.755723Z",
     "iopub.status.idle": "2024-12-16T03:13:17.763182Z",
     "shell.execute_reply": "2024-12-16T03:13:17.762179Z"
    },
    "papermill": {
     "duration": 0.022077,
     "end_time": "2024-12-16T03:13:17.765102",
     "exception": false,
     "start_time": "2024-12-16T03:13:17.743025",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 89.19,  86.  ,  91.12, ..., 212.65, 225.95, 231.96])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df.close.astype('float').values\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93dfca73",
   "metadata": {
    "papermill": {
     "duration": 0.011276,
     "end_time": "2024-12-16T03:13:17.788200",
     "exception": false,
     "start_time": "2024-12-16T03:13:17.776924",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Dividing data into train and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "45f4ab5d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:17.813612Z",
     "iopub.status.busy": "2024-12-16T03:13:17.813225Z",
     "iopub.status.idle": "2024-12-16T03:13:17.819440Z",
     "shell.execute_reply": "2024-12-16T03:13:17.818398Z"
    },
    "papermill": {
     "duration": 0.021242,
     "end_time": "2024-12-16T03:13:17.821544",
     "exception": false,
     "start_time": "2024-12-16T03:13:17.800302",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df, test_df = train_test_split(df,test_size=0.8,shuffle=False,random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "103b60a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:17.846579Z",
     "iopub.status.busy": "2024-12-16T03:13:17.846179Z",
     "iopub.status.idle": "2024-12-16T03:13:17.853000Z",
     "shell.execute_reply": "2024-12-16T03:13:17.851991Z"
    },
    "papermill": {
     "duration": 0.021788,
     "end_time": "2024-12-16T03:13:17.855002",
     "exception": false,
     "start_time": "2024-12-16T03:13:17.833214",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((221, 1), (887, 1))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9f8f038",
   "metadata": {
    "papermill": {
     "duration": 0.011117,
     "end_time": "2024-12-16T03:13:17.877728",
     "exception": false,
     "start_time": "2024-12-16T03:13:17.866611",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bdaa3c8a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:17.902226Z",
     "iopub.status.busy": "2024-12-16T03:13:17.901853Z",
     "iopub.status.idle": "2024-12-16T03:13:17.911166Z",
     "shell.execute_reply": "2024-12-16T03:13:17.910202Z"
    },
    "papermill": {
     "duration": 0.024049,
     "end_time": "2024-12-16T03:13:17.913127",
     "exception": false,
     "start_time": "2024-12-16T03:13:17.889078",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "train_norm = scaler.fit_transform(train_df)\n",
    "test_norm = scaler.transform(test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3733329",
   "metadata": {
    "papermill": {
     "duration": 0.011127,
     "end_time": "2024-12-16T03:13:17.935763",
     "exception": false,
     "start_time": "2024-12-16T03:13:17.924636",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Creating a custom Time Series Dataset class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "775e72b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:17.960130Z",
     "iopub.status.busy": "2024-12-16T03:13:17.959750Z",
     "iopub.status.idle": "2024-12-16T03:13:17.966070Z",
     "shell.execute_reply": "2024-12-16T03:13:17.965066Z"
    },
    "papermill": {
     "duration": 0.020961,
     "end_time": "2024-12-16T03:13:17.968066",
     "exception": false,
     "start_time": "2024-12-16T03:13:17.947105",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class TimeSeriesDataset(Dataset):\n",
    "\n",
    "    def __init__(self,sequence_data,sequence_length):\n",
    "        super().__init__()\n",
    "        self.sequence = torch.tensor(sequence_data,dtype=torch.float32)\n",
    "        self.sequence_length = sequence_length\n",
    "        self.num_samples = len(self.sequence) - self.sequence_length\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.num_samples\n",
    "\n",
    "    def __getitem__(self,idx):\n",
    "        window = self.sequence[idx:idx+self.sequence_length]\n",
    "        label = self.sequence[idx+self.sequence_length]\n",
    "        return (window,label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aadc566e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:17.992569Z",
     "iopub.status.busy": "2024-12-16T03:13:17.992194Z",
     "iopub.status.idle": "2024-12-16T03:13:18.034242Z",
     "shell.execute_reply": "2024-12-16T03:13:18.033168Z"
    },
    "papermill": {
     "duration": 0.057044,
     "end_time": "2024-12-16T03:13:18.036714",
     "exception": false,
     "start_time": "2024-12-16T03:13:17.979670",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.manual_seed(48)\n",
    "train_data = TimeSeriesDataset(train_norm,sequence_length=10)\n",
    "test_data = TimeSeriesDataset(test_norm,sequence_length=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba5f965e",
   "metadata": {
    "papermill": {
     "duration": 0.011221,
     "end_time": "2024-12-16T03:13:18.059962",
     "exception": false,
     "start_time": "2024-12-16T03:13:18.048741",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Creating Data Loaders for train and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8da4366c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:18.084417Z",
     "iopub.status.busy": "2024-12-16T03:13:18.084016Z",
     "iopub.status.idle": "2024-12-16T03:13:18.089541Z",
     "shell.execute_reply": "2024-12-16T03:13:18.088516Z"
    },
    "papermill": {
     "duration": 0.020219,
     "end_time": "2024-12-16T03:13:18.091635",
     "exception": false,
     "start_time": "2024-12-16T03:13:18.071416",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_data_loader = DataLoader(dataset=train_data,batch_size=10,shuffle=False,num_workers=5)\n",
    "test_data_loader = DataLoader(dataset=test_data,batch_size=10,shuffle=False,num_workers=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "43218517",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:18.116559Z",
     "iopub.status.busy": "2024-12-16T03:13:18.116175Z",
     "iopub.status.idle": "2024-12-16T03:13:19.718812Z",
     "shell.execute_reply": "2024-12-16T03:13:19.717441Z"
    },
    "papermill": {
     "duration": 1.617851,
     "end_time": "2024-12-16T03:13:19.721463",
     "exception": false,
     "start_time": "2024-12-16T03:13:18.103612",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for seq, targets in train_data_loader:\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dfa93c58",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:19.746915Z",
     "iopub.status.busy": "2024-12-16T03:13:19.746489Z",
     "iopub.status.idle": "2024-12-16T03:13:19.787929Z",
     "shell.execute_reply": "2024-12-16T03:13:19.786906Z"
    },
    "papermill": {
     "duration": 0.056742,
     "end_time": "2024-12-16T03:13:19.790192",
     "exception": false,
     "start_time": "2024-12-16T03:13:19.733450",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[0.6991],\n",
       "         [0.6651],\n",
       "         [0.7196],\n",
       "         [0.7729],\n",
       "         [0.7489],\n",
       "         [0.9767],\n",
       "         [1.0000],\n",
       "         [0.9927],\n",
       "         [0.9361],\n",
       "         [0.9447]]),\n",
       " tensor([0.8542]))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq[0], targets[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea4793bf",
   "metadata": {
    "papermill": {
     "duration": 0.011175,
     "end_time": "2024-12-16T03:13:19.812879",
     "exception": false,
     "start_time": "2024-12-16T03:13:19.801704",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Defining the LSTM model architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7855bc10",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:19.837599Z",
     "iopub.status.busy": "2024-12-16T03:13:19.837187Z",
     "iopub.status.idle": "2024-12-16T03:13:19.845103Z",
     "shell.execute_reply": "2024-12-16T03:13:19.844043Z"
    },
    "papermill": {
     "duration": 0.022785,
     "end_time": "2024-12-16T03:13:19.847198",
     "exception": false,
     "start_time": "2024-12-16T03:13:19.824413",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class LSTM(nn.Module):\n",
    "\n",
    "    def __init__(self,input_size=1,hidden_size=100,output_size=1):\n",
    "        super(LSTM,self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        self.lstm = nn.LSTM(input_size=input_size,hidden_size=hidden_size,batch_first=True)\n",
    "        self.linear = nn.Linear(in_features=hidden_size,out_features=output_size)\n",
    "        self.hidden = (torch.zeros(1,1,self.hidden_size),torch.zeros(1,1,self.hidden_size))\n",
    "\n",
    "    def forward(self,sequence):\n",
    "        batch_size = sequence.size(0)\n",
    "        self.hidden = self.init_hidden(batch_size)\n",
    "        lstm_output, self.hidden = self.lstm(sequence,self.hidden)\n",
    "        pred = self.linear(lstm_output[:,-1,:])\n",
    "        return pred\n",
    "\n",
    "    def init_hidden(self,batch_size):\n",
    "        return (torch.zeros(1,batch_size,self.hidden_size),torch.zeros(1,batch_size,self.hidden_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7cdfcbec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:19.872041Z",
     "iopub.status.busy": "2024-12-16T03:13:19.871665Z",
     "iopub.status.idle": "2024-12-16T03:13:19.885536Z",
     "shell.execute_reply": "2024-12-16T03:13:19.884504Z"
    },
    "papermill": {
     "duration": 0.028793,
     "end_time": "2024-12-16T03:13:19.887649",
     "exception": false,
     "start_time": "2024-12-16T03:13:19.858856",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSTM(\n",
       "  (lstm): LSTM(1, 150, batch_first=True)\n",
       "  (linear): Linear(in_features=150, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(48)\n",
    "model = LSTM(input_size=1,hidden_size=150)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d5e0144a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:19.912494Z",
     "iopub.status.busy": "2024-12-16T03:13:19.912082Z",
     "iopub.status.idle": "2024-12-16T03:13:19.918275Z",
     "shell.execute_reply": "2024-12-16T03:13:19.917027Z"
    },
    "papermill": {
     "duration": 0.021039,
     "end_time": "2024-12-16T03:13:19.920431",
     "exception": false,
     "start_time": "2024-12-16T03:13:19.899392",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600\n",
      "90000\n",
      "600\n",
      "600\n",
      "150\n",
      "1\n",
      "Total LSTM model parameters: 91951\n"
     ]
    }
   ],
   "source": [
    "total_params = 0\n",
    "\n",
    "for param in model.parameters():\n",
    "    print(param.numel())\n",
    "    total_params += param.numel()\n",
    "\n",
    "print(\"Total LSTM model parameters:\", total_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "df45282d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:19.945194Z",
     "iopub.status.busy": "2024-12-16T03:13:19.944819Z",
     "iopub.status.idle": "2024-12-16T03:13:19.953231Z",
     "shell.execute_reply": "2024-12-16T03:13:19.952158Z"
    },
    "papermill": {
     "duration": 0.023107,
     "end_time": "2024-12-16T03:13:19.955238",
     "exception": false,
     "start_time": "2024-12-16T03:13:19.932131",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "=================================================================\n",
       "Layer (type:depth-idx)                   Param #\n",
       "=================================================================\n",
       "LSTM                                     --\n",
       "├─LSTM: 1-1                              91,800\n",
       "├─Linear: 1-2                            151\n",
       "=================================================================\n",
       "Total params: 91,951\n",
       "Trainable params: 91,951\n",
       "Non-trainable params: 0\n",
       "================================================================="
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "543b4e27",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:02:16.631940Z",
     "iopub.status.busy": "2024-12-16T03:02:16.631498Z",
     "iopub.status.idle": "2024-12-16T03:02:16.637631Z",
     "shell.execute_reply": "2024-12-16T03:02:16.636232Z",
     "shell.execute_reply.started": "2024-12-16T03:02:16.631896Z"
    },
    "papermill": {
     "duration": 0.011925,
     "end_time": "2024-12-16T03:13:19.979305",
     "exception": false,
     "start_time": "2024-12-16T03:13:19.967380",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Model Training & Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "766f4414",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:20.005579Z",
     "iopub.status.busy": "2024-12-16T03:13:20.005157Z",
     "iopub.status.idle": "2024-12-16T03:13:21.037127Z",
     "shell.execute_reply": "2024-12-16T03:13:21.036074Z"
    },
    "papermill": {
     "duration": 1.047769,
     "end_time": "2024-12-16T03:13:21.039501",
     "exception": false,
     "start_time": "2024-12-16T03:13:19.991732",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.AdamW(params=model.parameters(),lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5fbaf44d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:21.065779Z",
     "iopub.status.busy": "2024-12-16T03:13:21.065175Z",
     "iopub.status.idle": "2024-12-16T03:13:49.364480Z",
     "shell.execute_reply": "2024-12-16T03:13:49.363054Z"
    },
    "papermill": {
     "duration": 28.315062,
     "end_time": "2024-12-16T03:13:49.366863",
     "exception": false,
     "start_time": "2024-12-16T03:13:21.051801",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 -> Loss: 0.12628515149382027\n",
      "Epoch 2 -> Loss: 0.049639066242062574\n",
      "Epoch 3 -> Loss: 0.0261874936404638\n",
      "Epoch 4 -> Loss: 0.030551062992096624\n",
      "Epoch 5 -> Loss: 0.013637871285688809\n",
      "Epoch 6 -> Loss: 0.009970689048879483\n",
      "Epoch 7 -> Loss: 0.008055377230696402\n",
      "Epoch 8 -> Loss: 0.008130337411304563\n",
      "Epoch 9 -> Loss: 0.00929417505937057\n",
      "Epoch 10 -> Loss: 0.01145900699669834\n",
      "Epoch 11 -> Loss: 0.01193891677857292\n",
      "Epoch 12 -> Loss: 0.008197609237289395\n",
      "Epoch 13 -> Loss: 0.0058824574088331165\n",
      "Epoch 14 -> Loss: 0.005750031329377883\n",
      "Epoch 15 -> Loss: 0.005090709622229703\n",
      "Epoch 16 -> Loss: 0.004873372044874652\n",
      "Epoch 17 -> Loss: 0.004844319040950557\n",
      "Epoch 18 -> Loss: 0.004875419655780372\n",
      "Epoch 19 -> Loss: 0.004935325368767901\n",
      "Epoch 20 -> Loss: 0.005010934809185776\n",
      "Epoch 21 -> Loss: 0.005020977152691392\n",
      "Epoch 22 -> Loss: 0.005103643474616216\n",
      "Epoch 23 -> Loss: 0.004988278104891916\n",
      "Epoch 24 -> Loss: 0.004961107628001959\n",
      "Epoch 25 -> Loss: 0.0047461767231156545\n",
      "Epoch 26 -> Loss: 0.00465300165425023\n",
      "Epoch 27 -> Loss: 0.004470529574477537\n",
      "Epoch 28 -> Loss: 0.004414239481279351\n",
      "Epoch 29 -> Loss: 0.0043131427770607515\n",
      "Epoch 30 -> Loss: 0.0043090697009492114\n",
      "Epoch 31 -> Loss: 0.0042395696178904145\n",
      "Epoch 32 -> Loss: 0.004267375053380171\n",
      "Epoch 33 -> Loss: 0.0041736372440936975\n",
      "Epoch 34 -> Loss: 0.004213291323222537\n",
      "Epoch 35 -> Loss: 0.004065110610099509\n",
      "Epoch 36 -> Loss: 0.004098830261351726\n",
      "Epoch 37 -> Loss: 0.003907219032870754\n",
      "Epoch 38 -> Loss: 0.00394179776404516\n",
      "Epoch 39 -> Loss: 0.0037480530875406907\n",
      "Epoch 40 -> Loss: 0.0038101812913075924\n",
      "Epoch 41 -> Loss: 0.0036317842254489237\n",
      "Epoch 42 -> Loss: 0.003746489341833384\n",
      "Epoch 43 -> Loss: 0.0035464245011098683\n",
      "Epoch 44 -> Loss: 0.0037333167701118245\n",
      "Epoch 45 -> Loss: 0.003454722725357649\n",
      "Epoch 46 -> Loss: 0.0037072241686308353\n",
      "Epoch 47 -> Loss: 0.003338682508911006\n",
      "Epoch 48 -> Loss: 0.0036052216415886173\n",
      "Epoch 49 -> Loss: 0.0032040198401029393\n",
      "Epoch 50 -> Loss: 0.003424621748060665\n",
      "Epoch 51 -> Loss: 0.003063137349355119\n",
      "Epoch 52 -> Loss: 0.0032150644596681973\n",
      "Epoch 53 -> Loss: 0.0029235629031476988\n",
      "Epoch 54 -> Loss: 0.0030207782418239567\n",
      "Epoch 55 -> Loss: 0.002784170815588864\n",
      "Epoch 56 -> Loss: 0.002853521876452512\n",
      "Epoch 57 -> Loss: 0.0026416643330859106\n",
      "Epoch 58 -> Loss: 0.0027117726490773334\n",
      "Epoch 59 -> Loss: 0.002504594744757262\n",
      "Epoch 60 -> Loss: 0.0025993139977138276\n",
      "Epoch 61 -> Loss: 0.0023818452422843125\n",
      "Epoch 62 -> Loss: 0.002472859894210573\n",
      "Epoch 63 -> Loss: 0.002264163502497302\n",
      "Epoch 64 -> Loss: 0.002316657521160695\n",
      "Epoch 65 -> Loss: 0.0022054279870644677\n",
      "Epoch 66 -> Loss: 0.002233820998051008\n",
      "Epoch 67 -> Loss: 0.0021933248393576255\n",
      "Epoch 68 -> Loss: 0.002223539167640741\n",
      "Epoch 69 -> Loss: 0.0021546840679630723\n",
      "Epoch 70 -> Loss: 0.0022226209617987147\n",
      "Epoch 71 -> Loss: 0.002097927670547506\n",
      "Epoch 72 -> Loss: 0.0021919003926417313\n",
      "Epoch 73 -> Loss: 0.0020467249510561073\n",
      "Epoch 74 -> Loss: 0.0021331166004033134\n",
      "Epoch 75 -> Loss: 0.0020062353889277965\n",
      "Epoch 76 -> Loss: 0.0020724381962612492\n",
      "Epoch 77 -> Loss: 0.0019758965638836594\n",
      "Epoch 78 -> Loss: 0.0020277437729419034\n",
      "Epoch 79 -> Loss: 0.0019513855057249946\n",
      "Epoch 80 -> Loss: 0.0019991907812378818\n",
      "Epoch 81 -> Loss: 0.0019293900006067072\n",
      "Epoch 82 -> Loss: 0.001979420461773109\n",
      "Epoch 83 -> Loss: 0.001909137536792762\n",
      "Epoch 84 -> Loss: 0.0019620674209735707\n",
      "Epoch 85 -> Loss: 0.0018904872567046286\n",
      "Epoch 86 -> Loss: 0.0019443478001903713\n",
      "Epoch 87 -> Loss: 0.0018733955494099064\n",
      "Epoch 88 -> Loss: 0.0019264556371622762\n",
      "Epoch 89 -> Loss: 0.001858024328777736\n",
      "Epoch 90 -> Loss: 0.0019097202922363397\n",
      "Epoch 91 -> Loss: 0.001844452224428427\n",
      "Epoch 92 -> Loss: 0.001895104332871514\n",
      "Epoch 93 -> Loss: 0.0018324775148192368\n",
      "Epoch 94 -> Loss: 0.0018827525037233334\n",
      "Epoch 95 -> Loss: 0.0018217276223036936\n",
      "Epoch 96 -> Loss: 0.0018722430628176053\n",
      "Epoch 97 -> Loss: 0.0018118448585730882\n",
      "Epoch 98 -> Loss: 0.0018629827345648664\n",
      "Epoch 99 -> Loss: 0.0018025960800349078\n",
      "Epoch 100 -> Loss: 0.0018544566232363154\n",
      "Total time elapsed in LSTM model training: 0.47151474952697753 minutes.\n"
     ]
    }
   ],
   "source": [
    "epochs = 100\n",
    "\n",
    "losses = []\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "for i in range(1,epochs+1):\n",
    "    epoch_loss = 0\n",
    "    \n",
    "    for seq, y_train in train_data_loader:\n",
    "        optimizer.zero_grad()\n",
    "        y_pred = model(seq)\n",
    "        loss = criterion(y_pred,y_train)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        epoch_loss += loss.item()\n",
    "\n",
    "    losses.append(epoch_loss/len(train_data_loader))\n",
    "    print(f\"Epoch {i} -> Loss: {epoch_loss/len(train_data_loader)}\")\n",
    "\n",
    "print(f\"Total time elapsed in LSTM model training: {(time.time()-start_time)/60} minutes.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cf239eec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:49.404320Z",
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     "shell.execute_reply": "2024-12-16T03:13:49.660608Z"
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    "papermill": {
     "duration": 0.278893,
     "end_time": "2024-12-16T03:13:49.663995",
     "exception": false,
     "start_time": "2024-12-16T03:13:49.385102",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,epochs+1),losses)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('MSE Loss')\n",
    "plt.title('Loss Graph')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "573cf058",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:49.700822Z",
     "iopub.status.busy": "2024-12-16T03:13:49.700458Z",
     "iopub.status.idle": "2024-12-16T03:13:50.128290Z",
     "shell.execute_reply": "2024-12-16T03:13:50.126873Z"
    },
    "papermill": {
     "duration": 0.449369,
     "end_time": "2024-12-16T03:13:50.131006",
     "exception": false,
     "start_time": "2024-12-16T03:13:49.681637",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average test loss: 0.0023\n"
     ]
    }
   ],
   "source": [
    "model.eval() # Set model to evaluation mode\n",
    "test_loss = 0\n",
    "all_predictions = []\n",
    "all_targets = []\n",
    "\n",
    "with torch.no_grad():\n",
    "\n",
    "    for seq, y_test in test_data_loader:\n",
    "        y_pred = model(seq)\n",
    "        loss = criterion(y_pred,y_test)\n",
    "        test_loss += loss.item()\n",
    "        all_predictions.append(y_pred)\n",
    "        all_targets.append(y_test)\n",
    "\n",
    "test_loss /= len(test_data_loader)\n",
    "all_predictions = np.concatenate(all_predictions)\n",
    "all_targets = np.concatenate(all_targets)\n",
    "print(\"Average test loss:\", round(test_loss,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ef4b1365",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:50.168532Z",
     "iopub.status.busy": "2024-12-16T03:13:50.168095Z",
     "iopub.status.idle": "2024-12-16T03:13:50.177421Z",
     "shell.execute_reply": "2024-12-16T03:13:50.176198Z"
    },
    "papermill": {
     "duration": 0.030339,
     "end_time": "2024-12-16T03:13:50.179561",
     "exception": false,
     "start_time": "2024-12-16T03:13:50.149222",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0175969 ],\n",
       "       [0.01472273],\n",
       "       [0.02951889],\n",
       "       [0.02670479],\n",
       "       [0.0207421 ]], dtype=float32)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_predictions[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f362c598",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:50.217294Z",
     "iopub.status.busy": "2024-12-16T03:13:50.216915Z",
     "iopub.status.idle": "2024-12-16T03:13:50.223847Z",
     "shell.execute_reply": "2024-12-16T03:13:50.222792Z"
    },
    "papermill": {
     "duration": 0.027939,
     "end_time": "2024-12-16T03:13:50.225779",
     "exception": false,
     "start_time": "2024-12-16T03:13:50.197840",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.01704303],\n",
       "       [0.04239454],\n",
       "       [0.02737537],\n",
       "       [0.02364721],\n",
       "       [0.02449936]], dtype=float32)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_targets[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "be8e7fbf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:50.263592Z",
     "iopub.status.busy": "2024-12-16T03:13:50.262849Z",
     "iopub.status.idle": "2024-12-16T03:13:50.270297Z",
     "shell.execute_reply": "2024-12-16T03:13:50.269186Z"
    },
    "papermill": {
     "duration": 0.028337,
     "end_time": "2024-12-16T03:13:50.272280",
     "exception": false,
     "start_time": "2024-12-16T03:13:50.243943",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[25.211998],\n",
       "       [24.942171],\n",
       "       [26.331232],\n",
       "       [26.067047],\n",
       "       [25.507269]], dtype=float32)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_predictions = scaler.inverse_transform(all_predictions.reshape(-1,1))\n",
    "true_predictions[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ad0ed79e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:50.309973Z",
     "iopub.status.busy": "2024-12-16T03:13:50.309161Z",
     "iopub.status.idle": "2024-12-16T03:13:50.316367Z",
     "shell.execute_reply": "2024-12-16T03:13:50.315380Z"
    },
    "papermill": {
     "duration": 0.027647,
     "end_time": "2024-12-16T03:13:50.318268",
     "exception": false,
     "start_time": "2024-12-16T03:13:50.290621",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[25.16    ],\n",
       "       [27.54    ],\n",
       "       [26.130001],\n",
       "       [25.779999],\n",
       "       [25.86    ]], dtype=float32)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual_values = scaler.inverse_transform(all_targets.reshape(-1,1))\n",
    "actual_values[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "81098ced",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:50.355409Z",
     "iopub.status.busy": "2024-12-16T03:13:50.354570Z",
     "iopub.status.idle": "2024-12-16T03:13:50.364549Z",
     "shell.execute_reply": "2024-12-16T03:13:50.363548Z"
    },
    "papermill": {
     "duration": 0.030906,
     "end_time": "2024-12-16T03:13:50.366691",
     "exception": false,
     "start_time": "2024-12-16T03:13:50.335785",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 2.529215\n",
      "MSE: 19.216318\n",
      "MAPE: 0.03847716\n",
      "MSLE: 0.0021951494\n",
      "RMSE: 4.383642\n"
     ]
    }
   ],
   "source": [
    "print(\"MAE:\", mean_absolute_error(actual_values,true_predictions))\n",
    "print(\"MSE:\", mean_squared_error(actual_values,true_predictions))\n",
    "print(\"MAPE:\", mean_absolute_percentage_error(actual_values,true_predictions))\n",
    "print(\"MSLE:\", mean_squared_log_error(actual_values,true_predictions))\n",
    "print(\"RMSE:\", np.sqrt(mean_squared_error(actual_values,true_predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d7b43ba3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:50.404202Z",
     "iopub.status.busy": "2024-12-16T03:13:50.403463Z",
     "iopub.status.idle": "2024-12-16T03:13:50.410568Z",
     "shell.execute_reply": "2024-12-16T03:13:50.409559Z"
    },
    "papermill": {
     "duration": 0.027929,
     "end_time": "2024-12-16T03:13:50.412497",
     "exception": false,
     "start_time": "2024-12-16T03:13:50.384568",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2004-02-06', '2004-02-13', '2004-02-20', '2004-02-27', '2004-03-05',\n",
       "       '2004-03-12', '2004-03-19', '2004-03-26', '2004-04-02', '2004-04-08',\n",
       "       ...\n",
       "       '2020-11-27', '2020-12-04', '2020-12-11', '2020-12-18', '2020-12-24',\n",
       "       '2020-12-31', '2021-01-08', '2021-01-15', '2021-01-22', '2021-01-29'],\n",
       "      dtype='object', length=887)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "62d1fb08",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:50.450411Z",
     "iopub.status.busy": "2024-12-16T03:13:50.450021Z",
     "iopub.status.idle": "2024-12-16T03:13:50.470544Z",
     "shell.execute_reply": "2024-12-16T03:13:50.469412Z"
    },
    "papermill": {
     "duration": 0.041669,
     "end_time": "2024-12-16T03:13:50.472683",
     "exception": false,
     "start_time": "2024-12-16T03:13:50.431014",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2004-02-08', '2004-02-15', '2004-02-22', '2004-02-29',\n",
       "               '2004-03-07', '2004-03-14', '2004-03-21', '2004-03-28',\n",
       "               '2004-04-04', '2004-04-11',\n",
       "               ...\n",
       "               '2020-09-20', '2020-09-27', '2020-10-04', '2020-10-11',\n",
       "               '2020-10-18', '2020-10-25', '2020-11-01', '2020-11-08',\n",
       "               '2020-11-15', '2020-11-22'],\n",
       "              dtype='datetime64[ns]', length=877, freq='W-SUN')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range(start='2004-02-06',end='2021-01-29',freq='W')\n",
    "dates = dates[:len(true_predictions)]\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "026fb75a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:50.510751Z",
     "iopub.status.busy": "2024-12-16T03:13:50.510311Z",
     "iopub.status.idle": "2024-12-16T03:13:50.515069Z",
     "shell.execute_reply": "2024-12-16T03:13:50.514102Z"
    },
    "papermill": {
     "duration": 0.026181,
     "end_time": "2024-12-16T03:13:50.517124",
     "exception": false,
     "start_time": "2024-12-16T03:13:50.490943",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "true_predictions = true_predictions.flatten()\n",
    "actual_values = actual_values.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b71ff3a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:50.555426Z",
     "iopub.status.busy": "2024-12-16T03:13:50.555020Z",
     "iopub.status.idle": "2024-12-16T03:13:50.937075Z",
     "shell.execute_reply": "2024-12-16T03:13:50.935718Z"
    },
    "papermill": {
     "duration": 0.404121,
     "end_time": "2024-12-16T03:13:50.939392",
     "exception": false,
     "start_time": "2024-12-16T03:13:50.535271",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(dates, actual_values, label='Actual Values', color='blue')\n",
    "plt.plot(dates, true_predictions, label='Predicted Values', color='orange')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Values')\n",
    "plt.title('Time Series Forecast: Predictions vs Actuals')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0faffdbd",
   "metadata": {
    "papermill": {
     "duration": 0.01888,
     "end_time": "2024-12-16T03:13:50.978311",
     "exception": false,
     "start_time": "2024-12-16T03:13:50.959431",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Re-training the model on whole data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b03d7ed2",
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   "source": [
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=1e-4)  # Lower learning rate\n",
    "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5)  # Reduce LR every 20 epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
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    "execution": {
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   "source": [
    "# Custom function to initialize model weights using Xavier initialization\n",
    "def init_weights(m):\n",
    "    if isinstance(m, nn.Linear):\n",
    "        nn.init.xavier_uniform_(m.weight)\n",
    "        if m.bias is not None:\n",
    "            nn.init.zeros_(m.bias)\n",
    "    elif isinstance(m, nn.LSTM):\n",
    "        for name, param in m.named_parameters():\n",
    "            if \"weight\" in name:\n",
    "                nn.init.xavier_uniform_(param)\n",
    "            elif \"bias\" in name:\n",
    "                nn.init.zeros_(param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1efc991a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:13:51.115505Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSTM(\n",
       "  (lstm): LSTM(1, 150, batch_first=True)\n",
       "  (linear): Linear(in_features=150, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.apply(init_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c220f294",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 -> Loss: 0.14320059157828588\n",
      "Epoch 2 -> Loss: 0.024865446476509203\n",
      "Epoch 3 -> Loss: 0.012555189360930606\n",
      "Epoch 4 -> Loss: 0.00835224178147407\n",
      "Epoch 5 -> Loss: 0.006896023116364218\n",
      "Epoch 6 -> Loss: 0.005825404424066702\n",
      "Epoch 7 -> Loss: 0.005248936396434477\n",
      "Epoch 8 -> Loss: 0.004714659325294302\n",
      "Epoch 9 -> Loss: 0.0041182180491060455\n",
      "Epoch 10 -> Loss: 0.0037288215434686705\n",
      "Epoch 11 -> Loss: 0.003527568988482182\n",
      "Epoch 12 -> Loss: 0.003432557858600789\n",
      "Epoch 13 -> Loss: 0.003410417248067626\n",
      "Epoch 14 -> Loss: 0.0034393720503761538\n",
      "Epoch 15 -> Loss: 0.0035021052318453174\n",
      "Epoch 16 -> Loss: 0.003582643120766575\n",
      "Epoch 17 -> Loss: 0.0036655978435605487\n",
      "Epoch 18 -> Loss: 0.003737301629239069\n",
      "Epoch 19 -> Loss: 0.0037816078392180234\n",
      "Epoch 20 -> Loss: 0.0037960958891000533\n",
      "Epoch 21 -> Loss: 0.004239339556848294\n",
      "Epoch 22 -> Loss: 0.0028717298027913785\n",
      "Epoch 23 -> Loss: 0.0022136015171285678\n",
      "Epoch 24 -> Loss: 0.002269674526236155\n",
      "Epoch 25 -> Loss: 0.0022850349592111094\n",
      "Epoch 26 -> Loss: 0.002301589341302381\n",
      "Epoch 27 -> Loss: 0.002317060484876327\n",
      "Epoch 28 -> Loss: 0.002330440254643856\n",
      "Epoch 29 -> Loss: 0.002341474451730557\n",
      "Epoch 30 -> Loss: 0.002350212517558248\n",
      "Epoch 31 -> Loss: 0.002356846305933273\n",
      "Epoch 32 -> Loss: 0.0023616141888024604\n",
      "Epoch 33 -> Loss: 0.0023647507341758517\n",
      "Epoch 34 -> Loss: 0.00236646317647352\n",
      "Epoch 35 -> Loss: 0.002366924109512845\n",
      "Epoch 36 -> Loss: 0.002366275147671166\n",
      "Epoch 37 -> Loss: 0.0023646326711142056\n",
      "Epoch 38 -> Loss: 0.002362090563143614\n",
      "Epoch 39 -> Loss: 0.0023587279194047864\n",
      "Epoch 40 -> Loss: 0.002354617305569594\n",
      "Epoch 41 -> Loss: 0.0024190958928350234\n",
      "Epoch 42 -> Loss: 0.0021144924731991274\n",
      "Epoch 43 -> Loss: 0.002047728910011673\n",
      "Epoch 44 -> Loss: 0.002057118355250251\n",
      "Epoch 45 -> Loss: 0.002050251454106315\n",
      "Epoch 46 -> Loss: 0.002051324393935581\n",
      "Epoch 47 -> Loss: 0.0020489288474147375\n",
      "Epoch 48 -> Loss: 0.002047624524592803\n",
      "Epoch 49 -> Loss: 0.0020455247670807613\n",
      "Epoch 50 -> Loss: 0.002043376750275043\n",
      "Epoch 51 -> Loss: 0.0020409013184524323\n",
      "Epoch 52 -> Loss: 0.002038223034535556\n",
      "Epoch 53 -> Loss: 0.0020353205743958826\n",
      "Epoch 54 -> Loss: 0.0020322268640566936\n",
      "Epoch 55 -> Loss: 0.0020289552503031287\n",
      "Epoch 56 -> Loss: 0.0020255266703646736\n",
      "Epoch 57 -> Loss: 0.0020219577789909885\n",
      "Epoch 58 -> Loss: 0.0020182649289298836\n",
      "Epoch 59 -> Loss: 0.002014463513610694\n",
      "Epoch 60 -> Loss: 0.002010567061138731\n",
      "Epoch 61 -> Loss: 0.0020066992100634127\n",
      "Epoch 62 -> Loss: 0.0019356403809069889\n",
      "Epoch 63 -> Loss: 0.0019286276168713813\n",
      "Epoch 64 -> Loss: 0.0019234029265961991\n",
      "Epoch 65 -> Loss: 0.001921816172084205\n",
      "Epoch 66 -> Loss: 0.001919520273019192\n",
      "Epoch 67 -> Loss: 0.001917820900408432\n",
      "Epoch 68 -> Loss: 0.001915869496869246\n",
      "Epoch 69 -> Loss: 0.0019140126554372148\n",
      "Epoch 70 -> Loss: 0.001912067768907119\n",
      "Epoch 71 -> Loss: 0.001910121650135724\n",
      "Epoch 72 -> Loss: 0.0019081339257023832\n",
      "Epoch 73 -> Loss: 0.0019061266453718416\n",
      "Epoch 74 -> Loss: 0.0019040930602469332\n",
      "Epoch 75 -> Loss: 0.0019020398750481862\n",
      "Epoch 76 -> Loss: 0.001899966534719252\n",
      "Epoch 77 -> Loss: 0.0018978769237368066\n",
      "Epoch 78 -> Loss: 0.0018957717878038238\n",
      "Epoch 79 -> Loss: 0.0018936530670378654\n",
      "Epoch 80 -> Loss: 0.0018915234193462476\n",
      "Epoch 81 -> Loss: 0.0018808424116286914\n",
      "Epoch 82 -> Loss: 0.0018665921878643777\n",
      "Epoch 83 -> Loss: 0.001861452605283954\n",
      "Epoch 84 -> Loss: 0.001859837441845064\n",
      "Epoch 85 -> Loss: 0.001858252197226565\n",
      "Epoch 86 -> Loss: 0.0018569790623670004\n",
      "Epoch 87 -> Loss: 0.0018558019786806572\n",
      "Epoch 88 -> Loss: 0.001854685033148681\n",
      "Epoch 89 -> Loss: 0.0018535955770361894\n",
      "Epoch 90 -> Loss: 0.0018525200102637676\n",
      "Epoch 91 -> Loss: 0.0018514499725576818\n",
      "Epoch 92 -> Loss: 0.001850381953805812\n",
      "Epoch 93 -> Loss: 0.0018493133545648148\n",
      "Epoch 94 -> Loss: 0.0018482433776129752\n",
      "Epoch 95 -> Loss: 0.001847172395851389\n",
      "Epoch 96 -> Loss: 0.0018460992712127997\n",
      "Epoch 97 -> Loss: 0.0018450249862326019\n",
      "Epoch 98 -> Loss: 0.0018439493414172665\n",
      "Epoch 99 -> Loss: 0.001842871682468037\n",
      "Epoch 100 -> Loss: 0.0018417936136756908\n",
      "Total time elapsed in LSTM model retraining: 1.4200425028800965 minutes.\n"
     ]
    }
   ],
   "source": [
    "epochs = 100\n",
    "\n",
    "y_norm = scaler.transform(y.reshape(-1,1))\n",
    "all_data = TimeSeriesDataset(sequence_data=y_norm,sequence_length=10)\n",
    "all_data_loader = DataLoader(dataset=all_data,batch_size=10,shuffle=False,num_workers=5)\n",
    "\n",
    "model.train()\n",
    "\n",
    "losses = []\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "for i in range(1,epochs+1):\n",
    "\n",
    "    epoch_loss = 0\n",
    "    \n",
    "    for seq, y_train in all_data_loader:\n",
    "        optimizer.zero_grad()\n",
    "        y_pred = model(seq)\n",
    "        loss = criterion(y_pred,y_train)\n",
    "        loss.backward()\n",
    "        clip_grad_norm_(parameters=model.parameters(),max_norm=1.0) # Gradient clipping\n",
    "        optimizer.step()\n",
    "        epoch_loss += loss.item()\n",
    "\n",
    "    epoch_loss /= len(all_data_loader)\n",
    "    scheduler.step() # Adjust learning rate\n",
    "    losses.append(epoch_loss)\n",
    "    print(f\"Epoch {i} -> Loss: {epoch_loss}\")\n",
    "\n",
    "print(f\"Total time elapsed in LSTM model retraining: {(time.time()-start_time)/60} minutes.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "984a123c",
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   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,epochs+1),losses)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('MSE Loss')\n",
    "plt.title('Loss Graph')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e213296",
   "metadata": {
    "papermill": {
     "duration": 0.024331,
     "end_time": "2024-12-16T03:15:16.830274",
     "exception": false,
     "start_time": "2024-12-16T03:15:16.805943",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Forecasting into unknown future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "26be7281",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:15:16.881456Z",
     "iopub.status.busy": "2024-12-16T03:15:16.881036Z",
     "iopub.status.idle": "2024-12-16T03:15:16.976893Z",
     "shell.execute_reply": "2024-12-16T03:15:16.976002Z"
    },
    "papermill": {
     "duration": 0.124336,
     "end_time": "2024-12-16T03:15:16.979242",
     "exception": false,
     "start_time": "2024-12-16T03:15:16.854906",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "forecast_horizon = 120\n",
    "sequence_length = 20\n",
    "predictions = y_norm[-sequence_length:].flatten().tolist()\n",
    "\n",
    "model.eval()\n",
    "\n",
    "for i in range(forecast_horizon):\n",
    "    sequence = torch.FloatTensor(predictions[-sequence_length:]).view(1, sequence_length, 1)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        predictions.append(model(sequence).item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f05fb4c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:15:17.030384Z",
     "iopub.status.busy": "2024-12-16T03:15:17.029974Z",
     "iopub.status.idle": "2024-12-16T03:15:17.036305Z",
     "shell.execute_reply": "2024-12-16T03:15:17.035391Z"
    },
    "papermill": {
     "duration": 0.033923,
     "end_time": "2024-12-16T03:15:17.038291",
     "exception": false,
     "start_time": "2024-12-16T03:15:17.004368",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "140"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "dff9eb8d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:15:17.089731Z",
     "iopub.status.busy": "2024-12-16T03:15:17.089313Z",
     "iopub.status.idle": "2024-12-16T03:15:17.095681Z",
     "shell.execute_reply": "2024-12-16T03:15:17.094726Z"
    },
    "papermill": {
     "duration": 0.035099,
     "end_time": "2024-12-16T03:15:17.097966",
     "exception": false,
     "start_time": "2024-12-16T03:15:17.062867",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2.76037859916687,\n",
       " 2.7605721950531006,\n",
       " 2.760755777359009,\n",
       " 2.7609293460845947,\n",
       " 2.7610933780670166]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[-sequence_length:][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e209e485",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:15:17.150395Z",
     "iopub.status.busy": "2024-12-16T03:15:17.149944Z",
     "iopub.status.idle": "2024-12-16T03:15:17.158316Z",
     "shell.execute_reply": "2024-12-16T03:15:17.157258Z"
    },
    "papermill": {
     "duration": 0.037583,
     "end_time": "2024-12-16T03:15:17.160266",
     "exception": false,
     "start_time": "2024-12-16T03:15:17.122683",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[200.39],\n",
       "       [207.82],\n",
       "       [206.19],\n",
       "       [215.81],\n",
       "       [219.66]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_predictions = scaler.inverse_transform(np.array(predictions).reshape(-1,1))\n",
    "true_predictions[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4c9f1897",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:15:17.211622Z",
     "iopub.status.busy": "2024-12-16T03:15:17.211236Z",
     "iopub.status.idle": "2024-12-16T03:15:17.219529Z",
     "shell.execute_reply": "2024-12-16T03:15:17.218430Z"
    },
    "papermill": {
     "duration": 0.035869,
     "end_time": "2024-12-16T03:15:17.221412",
     "exception": false,
     "start_time": "2024-12-16T03:15:17.185543",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[89.19],\n",
       "       [86.  ],\n",
       "       [91.12],\n",
       "       [96.12],\n",
       "       [93.87]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual_values = scaler.inverse_transform(np.array(y_norm).reshape(-1,1))\n",
    "actual_values[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e5a97545",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:15:17.273263Z",
     "iopub.status.busy": "2024-12-16T03:15:17.272911Z",
     "iopub.status.idle": "2024-12-16T03:15:17.288141Z",
     "shell.execute_reply": "2024-12-16T03:15:17.287112Z"
    },
    "papermill": {
     "duration": 0.043395,
     "end_time": "2024-12-16T03:15:17.290362",
     "exception": false,
     "start_time": "2024-12-16T03:15:17.246967",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1999-11-12', '1999-11-19', '1999-11-26', '1999-12-03',\n",
       "               '1999-12-10', '1999-12-17', '1999-12-23', '1999-12-31',\n",
       "               '2000-01-07', '2000-01-14',\n",
       "               ...\n",
       "               '2020-11-27', '2020-12-04', '2020-12-11', '2020-12-18',\n",
       "               '2020-12-24', '2020-12-31', '2021-01-08', '2021-01-15',\n",
       "               '2021-01-22', '2021-01-29'],\n",
       "              dtype='datetime64[ns]', length=1108, freq=None)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index = pd.to_datetime(df.index)\n",
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "94e36a0f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:15:17.343054Z",
     "iopub.status.busy": "2024-12-16T03:15:17.342662Z",
     "iopub.status.idle": "2024-12-16T03:15:17.353148Z",
     "shell.execute_reply": "2024-12-16T03:15:17.352053Z"
    },
    "papermill": {
     "duration": 0.039079,
     "end_time": "2024-12-16T03:15:17.355288",
     "exception": false,
     "start_time": "2024-12-16T03:15:17.316209",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2021-02-07', '2021-02-14', '2021-02-21', '2021-02-28',\n",
       "               '2021-03-07', '2021-03-14', '2021-03-21', '2021-03-28',\n",
       "               '2021-04-04', '2021-04-11',\n",
       "               ...\n",
       "               '2023-03-19', '2023-03-26', '2023-04-02', '2023-04-09',\n",
       "               '2023-04-16', '2023-04-23', '2023-04-30', '2023-05-07',\n",
       "               '2023-05-14', '2023-05-21'],\n",
       "              dtype='datetime64[ns]', length=120, freq='W-SUN')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future_dates = pd.date_range(start=df.index[-1]+pd.Timedelta(weeks=1),\n",
    "                             periods=forecast_horizon,\n",
    "                             freq='W')\n",
    "future_dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "879f233f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:15:17.408158Z",
     "iopub.status.busy": "2024-12-16T03:15:17.407505Z",
     "iopub.status.idle": "2024-12-16T03:15:17.414156Z",
     "shell.execute_reply": "2024-12-16T03:15:17.413125Z"
    },
    "papermill": {
     "duration": 0.03576,
     "end_time": "2024-12-16T03:15:17.416181",
     "exception": false,
     "start_time": "2024-12-16T03:15:17.380421",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120, 120)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(future_dates), len(true_predictions[sequence_length:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4a5af62a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:15:17.468310Z",
     "iopub.status.busy": "2024-12-16T03:15:17.467927Z",
     "iopub.status.idle": "2024-12-16T03:15:17.766170Z",
     "shell.execute_reply": "2024-12-16T03:15:17.764958Z"
    },
    "papermill": {
     "duration": 0.327522,
     "end_time": "2024-12-16T03:15:17.768810",
     "exception": false,
     "start_time": "2024-12-16T03:15:17.441288",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,5))\n",
    "plt.title('Global Equity Forecast')\n",
    "plt.ylabel('Global Equity')\n",
    "plt.grid(visible=True)\n",
    "plt.autoscale(enable=True,axis='x',tight=True)\n",
    "plt.plot(df.index,actual_values)\n",
    "plt.plot(future_dates,true_predictions[sequence_length:])\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d079af44",
   "metadata": {
    "papermill": {
     "duration": 0.025536,
     "end_time": "2024-12-16T03:15:17.820481",
     "exception": false,
     "start_time": "2024-12-16T03:15:17.794945",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Saving the model for future use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "bead867b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-16T03:15:17.874237Z",
     "iopub.status.busy": "2024-12-16T03:15:17.873864Z",
     "iopub.status.idle": "2024-12-16T03:15:17.880897Z",
     "shell.execute_reply": "2024-12-16T03:15:17.879950Z"
    },
    "papermill": {
     "duration": 0.03672,
     "end_time": "2024-12-16T03:15:17.882966",
     "exception": false,
     "start_time": "2024-12-16T03:15:17.846246",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.save(model,'global_equity_forecaster.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b65c85d",
   "metadata": {
    "papermill": {
     "duration": 0.025992,
     "end_time": "2024-12-16T03:15:17.936737",
     "exception": false,
     "start_time": "2024-12-16T03:15:17.910745",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "This is a relatively decent forecast considering the simplicity of the LSTM model. Please upvote my notebook if you really liked my work. In case you have any suggestions regarding my work, you can let me know through the comments section below."
   ]
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [
    {
     "datasetId": 1128031,
     "sourceId": 1893186,
     "sourceType": "datasetVersion"
    }
   ],
   "dockerImageVersionId": 30804,
   "isGpuEnabled": false,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "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": "3.10.14"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 142.546245,
   "end_time": "2024-12-16T03:15:20.318072",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-12-16T03:12:57.771827",
   "version": "2.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}