convolutional_net.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7418f6e4",
   "metadata": {},
   "source": [
    "# Convolutional Neural Networks\n",
    "\n",
    "_Life can only be understood backwards; but it must be lived forwards._ - Søren Kierkegaard\n",
    "\n",
    "Concepts\n",
    "- Temporal Convolutional Net (TCN)\n",
    "- Vector Autoregression\n",
    "\n",
    "References\n",
    "- Philipp Krahenbuhl, 2020, \"CS395T Deep Learning course materials\", retrieved from http://www.philkr.net/dl_class/material"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c4cf89be",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchinfo\n",
    "from statsmodels.tsa.api import VAR\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from finds.structured import BusDay\n",
    "from finds.readers import Alfred\n",
    "from secret import credentials\n",
    "# %matplotlib qt\n",
    "VERBOSE = 0\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d9788e24",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train-test split date\n",
    "split_date = '2021-12-01'   # training period up to this date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "12781e31",
   "metadata": {},
   "outputs": [],
   "source": [
    "# up to max number p of VAR(p) lags\n",
    "maxlags = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ee309282",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "alf = Alfred(api_key=credentials['fred']['api_key'], verbose=-1)\n",
    "vspans = alf.date_spans('USREC')    # recession periods for plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5d939b67",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_602057/4272565358.py:9: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  df.index.freq = 'M'     # set index to datetime type and freq = 'M'\n"
     ]
    }
   ],
   "source": [
    "# CPI for U.S. City Average: Monthly, Seasonally Adjusted\n",
    "# https://fred.stlouisfed.org/release/tables?rid=10&eid=34483\n",
    "# 'CUSR0000SEEA'\n",
    "series_ids = ['CPIFABSL', 'CPIHOSSL', 'CPIAPPSL', 'CPITRNSL', 'CPIMEDSL', 'CPIOGSSL']\n",
    "df = pd.concat([alf(s, log=1, diff=1) for s in series_ids], axis=1)\\\n",
    "       .dropna()\\\n",
    "       .sort_index()\n",
    "df.index = BusDay.to_datetime(df.index)\n",
    "df.index.freq = 'M'     # set index to datetime type and freq = 'M'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ab6b6f55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Food and Beverages',\n",
       " 'Housing',\n",
       " 'Apparel',\n",
       " 'Transportation',\n",
       " 'Medical Care',\n",
       " 'Other Goods and Services']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = [s[s.find(':')+2:s.find(' in ')] for s in alf.header(series_ids)]\n",
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "79dbdad9",
   "metadata": {},
   "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>Food and Beverages</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Apparel</th>\n",
       "      <th>Transportation</th>\n",
       "      <th>Medical Care</th>\n",
       "      <th>Other Goods and Services</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1967-02-28</th>\n",
       "      <td>-1.514294</td>\n",
       "      <td>-1.123374</td>\n",
       "      <td>0.528419</td>\n",
       "      <td>0.262158</td>\n",
       "      <td>-0.260945</td>\n",
       "      <td>-1.020565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967-03-31</th>\n",
       "      <td>-0.803761</td>\n",
       "      <td>-1.123374</td>\n",
       "      <td>0.117546</td>\n",
       "      <td>-0.270473</td>\n",
       "      <td>-0.265522</td>\n",
       "      <td>-0.290026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967-04-30</th>\n",
       "      <td>-1.516345</td>\n",
       "      <td>-0.064928</td>\n",
       "      <td>0.523552</td>\n",
       "      <td>0.258920</td>\n",
       "      <td>0.977131</td>\n",
       "      <td>-1.020565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967-05-31</th>\n",
       "      <td>-0.803761</td>\n",
       "      <td>-0.068382</td>\n",
       "      <td>0.115127</td>\n",
       "      <td>-0.006978</td>\n",
       "      <td>-0.279054</td>\n",
       "      <td>-0.292134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967-06-30</th>\n",
       "      <td>1.327849</td>\n",
       "      <td>-1.123374</td>\n",
       "      <td>0.518742</td>\n",
       "      <td>-0.270473</td>\n",
       "      <td>0.950357</td>\n",
       "      <td>-0.294231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31</th>\n",
       "      <td>-0.305673</td>\n",
       "      <td>-0.150090</td>\n",
       "      <td>-0.300364</td>\n",
       "      <td>-0.226813</td>\n",
       "      <td>-0.199913</td>\n",
       "      <td>-0.950240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-31</th>\n",
       "      <td>0.136279</td>\n",
       "      <td>0.874513</td>\n",
       "      <td>-1.728819</td>\n",
       "      <td>-0.823810</td>\n",
       "      <td>0.045640</td>\n",
       "      <td>0.340696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-29</th>\n",
       "      <td>-0.763532</td>\n",
       "      <td>0.154003</td>\n",
       "      <td>0.841913</td>\n",
       "      <td>0.932892</td>\n",
       "      <td>-1.610667</td>\n",
       "      <td>-1.786460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-31</th>\n",
       "      <td>-0.555699</td>\n",
       "      <td>0.155815</td>\n",
       "      <td>1.046454</td>\n",
       "      <td>0.409135</td>\n",
       "      <td>0.152838</td>\n",
       "      <td>-0.080117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-30</th>\n",
       "      <td>-0.757514</td>\n",
       "      <td>-0.408180</td>\n",
       "      <td>2.163913</td>\n",
       "      <td>0.318836</td>\n",
       "      <td>0.016674</td>\n",
       "      <td>0.048790</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>687 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Food and Beverages   Housing   Apparel  Transportation  \\\n",
       "date                                                                 \n",
       "1967-02-28           -1.514294 -1.123374  0.528419        0.262158   \n",
       "1967-03-31           -0.803761 -1.123374  0.117546       -0.270473   \n",
       "1967-04-30           -1.516345 -0.064928  0.523552        0.258920   \n",
       "1967-05-31           -0.803761 -0.068382  0.115127       -0.006978   \n",
       "1967-06-30            1.327849 -1.123374  0.518742       -0.270473   \n",
       "...                        ...       ...       ...             ...   \n",
       "2023-12-31           -0.305673 -0.150090 -0.300364       -0.226813   \n",
       "2024-01-31            0.136279  0.874513 -1.728819       -0.823810   \n",
       "2024-02-29           -0.763532  0.154003  0.841913        0.932892   \n",
       "2024-03-31           -0.555699  0.155815  1.046454        0.409135   \n",
       "2024-04-30           -0.757514 -0.408180  2.163913        0.318836   \n",
       "\n",
       "            Medical Care  Other Goods and Services  \n",
       "date                                                \n",
       "1967-02-28     -0.260945                 -1.020565  \n",
       "1967-03-31     -0.265522                 -0.290026  \n",
       "1967-04-30      0.977131                 -1.020565  \n",
       "1967-05-31     -0.279054                 -0.292134  \n",
       "1967-06-30      0.950357                 -0.294231  \n",
       "...                  ...                       ...  \n",
       "2023-12-31     -0.199913                 -0.950240  \n",
       "2024-01-31      0.045640                  0.340696  \n",
       "2024-02-29     -1.610667                 -1.786460  \n",
       "2024-03-31      0.152838                 -0.080117  \n",
       "2024-04-30      0.016674                  0.048790  \n",
       "\n",
       "[687 rows x 6 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standardize the data data\n",
    "scaler = StandardScaler().fit(df)\n",
    "scaled_data = DataFrame(scaler.transform(df), columns=names, index=df.index)\n",
    "scaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2474620d",
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [],
   "source": [
    "ntrain = sum(scaled_data.index < split_date)\n",
    "M = scaled_data.shape[1]      # M is number of time series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c427305",
   "metadata": {},
   "source": [
    "## Temporal Convolutional Net (TCN)\n",
    "\n",
    "- convolution as a filter such as a weighted average\n",
    "- hyperparameters: \n",
    "  - dropout\n",
    "  - hidden layers and size\n",
    "  - batch size and shuffling\n",
    "\n",
    "TODO:\n",
    "- example of 1D 2-channel and weighted average\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "655cbc79",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TCN(torch.nn.Module):\n",
    "    class CausalConv1dBlock(torch.nn.Module):\n",
    "        \"\"\"Conv1d block with ReLU, skip, dropout, dilation and padding\"\"\"\n",
    "        \n",
    "        def __init__(self, in_channels, out_channels, kernel_size, dilation,\n",
    "                     dropout):\n",
    "            super().__init__()\n",
    "\n",
    "            # print('kernel', kernel_size, 'dilation', dilation)\n",
    "            self.network = torch.nn.Sequential(\n",
    "                torch.nn.ConstantPad1d(((kernel_size-1)*dilation, 0), 0),\n",
    "                torch.nn.Conv1d(in_channels, out_channels, kernel_size,\n",
    "                                dilation=dilation),\n",
    "                torch.nn.ReLU(),\n",
    "                torch.nn.ConstantPad1d(((kernel_size-1)*dilation, 0), 0),\n",
    "                torch.nn.Conv1d(out_channels, out_channels, kernel_size,\n",
    "                                dilation=dilation),\n",
    "                torch.nn.ReLU(),\n",
    "                torch.nn.Dropout(dropout))\n",
    "            self.skip = lambda x: x\n",
    "            if in_channels != out_channels:   # downsample for skip if necessary\n",
    "                self.skip = torch.nn.Conv1d(in_channels, out_channels, 1)\n",
    "\n",
    "        def forward(self, x):\n",
    "            return self.network(x) + self.skip(x)  # with skip connection\n",
    "\n",
    "\n",
    "    def __init__(self, n_features, blocks, kernel_size, dropout):\n",
    "        \"\"\"TCN model by connecting multiple convolution layers\"\"\"\n",
    "        super().__init__()\n",
    "        in_channels = n_features\n",
    "        L = []\n",
    "        for dilation, hidden in enumerate(blocks):\n",
    "            L.append(self.CausalConv1dBlock(in_channels=in_channels,\n",
    "                                            out_channels=hidden,\n",
    "                                            kernel_size=kernel_size,\n",
    "                                            dilation=2**dilation,\n",
    "                                            dropout=dropout))\n",
    "            in_channels = hidden\n",
    "        self.network = torch.nn.Sequential(*L) if L else lambda x: x\n",
    "        if L:\n",
    "            self.classifier = torch.nn.Conv1d(in_channels, n_features, 1)\n",
    "        else:\n",
    "            self.classifier = torch.nn.Sequential(\n",
    "                torch.nn.ConstantPad1d((kernel_size-1, 0), 0),                \n",
    "                torch.nn.Conv1d(in_channels, n_features, kernel_size))\n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"input is (B, n_features, L)), linear expects (B, * n_features)\"\"\"\n",
    "        return self.classifier(self.network(x))\n",
    "\n",
    "    def save(self, filename):\n",
    "        \"\"\"save model state to filename\"\"\"\n",
    "        return torch.save(self.state_dict(), filename)\n",
    "\n",
    "    def load(self, filename):\n",
    "        \"\"\"load model name from filename\"\"\"\n",
    "        self.load_state_dict(torch.load(filename, map_location='cpu'))\n",
    "        return self"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e834da3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model training parameters\n",
    "seq_len = 8         # length of each input sequence for TCN\n",
    "batch_size = 16\n",
    "step_size = 100     # learning rate scheduler step size\n",
    "lr = 0.01           # initial learning rate\n",
    "num_lr = 3\n",
    "num_epochs = step_size * num_lr\n",
    "results = {}        # to collect evaluate results\n",
    "train_loss = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ab6eb3e9",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Form input data from training set\n",
    "n_features = scaled_data.shape[1]    # number of input planes\n",
    "train_exs = [scaled_data.iloc[(i - seq_len):(i + 1)].values\n",
    "             for i in range(seq_len, ntrain)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b677f7f8",
   "metadata": {},
   "source": [
    "Train a 1D Convolution Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b2ae6591",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conv1d(6, 6, kernel_size=(1,), stride=(1,))\n",
      "=================================================================\n",
      "Layer (type:depth-idx)                   Param #\n",
      "=================================================================\n",
      "Conv1d                                   42\n",
      "=================================================================\n",
      "Total params: 42\n",
      "Trainable params: 42\n",
      "Non-trainable params: 0\n",
      "=================================================================\n"
     ]
    }
   ],
   "source": [
    "model = torch.nn.Conv1d(n_features, n_features, kernel_size=1).to(device)\n",
    "print(model)\n",
    "print(torchinfo.summary(model))\n",
    "modelname = \"1D-Convolution\"\n",
    "train_loss[modelname] = []\n",
    "optimizer = torch.optim.Adam(model.parameters())\n",
    "loss_function = nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d0b6af1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_ex should have dimension (batch size, channels, sequence length+1)\n",
    "train_ex = torch.tensor(scaled_data.values[:ntrain].T)[None,:,:].float().to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e4c7d4cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "for epoch in range(num_epochs):\n",
    "    for _ in range(batch_size):\n",
    "        total_loss = 0.0\n",
    "        model.train()\n",
    "        model.zero_grad()\n",
    "        X = train_ex[:,:,:-1]\n",
    "        Y = train_ex[:,:,1:]\n",
    "        output = model(X)\n",
    "        loss = loss_function(output, Y)  # calculated over all outputs\n",
    "        total_loss += float(loss)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        model.eval()\n",
    "    train_loss[modelname].append(total_loss)\n",
    "    \n",
    "    X = torch.tensor(scaled_data.values.T)[None,:,:].float().to(device)\n",
    "    pred = model(X).cpu().detach().numpy()[0,:,:].T\n",
    "    results[modelname] = {\n",
    "        'Train Error': mean_squared_error(scaled_data.values[1:ntrain],\n",
    "                                          pred[:ntrain-1]),\n",
    "        'Test Error': mean_squared_error(scaled_data.values[ntrain:],\n",
    "                                         pred[ntrain-1:-1])\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0c174e5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "conv1d_weights = np.vstack([model.bias.cpu().detach().numpy(),\n",
    "                            model.weight.cpu().detach().numpy()[:,:,0].T])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6748384",
   "metadata": {},
   "source": [
    "Fit TCN models\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "30a4336c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "****** TCN(b=1,k=1,d=0.0) ******\n",
      "TCN(\n",
      "  (network): Sequential(\n",
      "    (0): CausalConv1dBlock(\n",
      "      (network): Sequential(\n",
      "        (0): ConstantPad1d(padding=(0, 0), value=0)\n",
      "        (1): Conv1d(6, 6, kernel_size=(1,), stride=(1,))\n",
      "        (2): ReLU()\n",
      "        (3): ConstantPad1d(padding=(0, 0), value=0)\n",
      "        (4): Conv1d(6, 6, kernel_size=(1,), stride=(1,))\n",
      "        (5): ReLU()\n",
      "        (6): Dropout(p=0, inplace=False)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (classifier): Conv1d(6, 6, kernel_size=(1,), stride=(1,))\n",
      ")\n",
      "=================================================================\n",
      "Layer (type:depth-idx)                   Param #\n",
      "=================================================================\n",
      "TCN                                      --\n",
      "├─Sequential: 1-1                        --\n",
      "│    └─CausalConv1dBlock: 2-1            --\n",
      "│    │    └─Sequential: 3-1              84\n",
      "├─Conv1d: 1-2                            42\n",
      "=================================================================\n",
      "Total params: 126\n",
      "Trainable params: 126\n",
      "Non-trainable params: 0\n",
      "=================================================================\n",
      "\n",
      "****** TCN(b=1,k=2,d=0.0) ******\n",
      "TCN(\n",
      "  (network): Sequential(\n",
      "    (0): CausalConv1dBlock(\n",
      "      (network): Sequential(\n",
      "        (0): ConstantPad1d(padding=(1, 0), value=0)\n",
      "        (1): Conv1d(6, 6, kernel_size=(2,), stride=(1,))\n",
      "        (2): ReLU()\n",
      "        (3): ConstantPad1d(padding=(1, 0), value=0)\n",
      "        (4): Conv1d(6, 6, kernel_size=(2,), stride=(1,))\n",
      "        (5): ReLU()\n",
      "        (6): Dropout(p=0, inplace=False)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (classifier): Conv1d(6, 6, kernel_size=(1,), stride=(1,))\n",
      ")\n",
      "=================================================================\n",
      "Layer (type:depth-idx)                   Param #\n",
      "=================================================================\n",
      "TCN                                      --\n",
      "├─Sequential: 1-1                        --\n",
      "│    └─CausalConv1dBlock: 2-1            --\n",
      "│    │    └─Sequential: 3-1              156\n",
      "├─Conv1d: 1-2                            42\n",
      "=================================================================\n",
      "Total params: 198\n",
      "Trainable params: 198\n",
      "Non-trainable params: 0\n",
      "=================================================================\n",
      "\n",
      "****** TCN(b=2,k=1,d=0.0) ******\n",
      "TCN(\n",
      "  (network): Sequential(\n",
      "    (0): CausalConv1dBlock(\n",
      "      (network): Sequential(\n",
      "        (0): ConstantPad1d(padding=(0, 0), value=0)\n",
      "        (1): Conv1d(6, 6, kernel_size=(1,), stride=(1,))\n",
      "        (2): ReLU()\n",
      "        (3): ConstantPad1d(padding=(0, 0), value=0)\n",
      "        (4): Conv1d(6, 6, kernel_size=(1,), stride=(1,))\n",
      "        (5): ReLU()\n",
      "        (6): Dropout(p=0, inplace=False)\n",
      "      )\n",
      "    )\n",
      "    (1): CausalConv1dBlock(\n",
      "      (network): Sequential(\n",
      "        (0): ConstantPad1d(padding=(0, 0), value=0)\n",
      "        (1): Conv1d(6, 6, kernel_size=(1,), stride=(1,), dilation=(2,))\n",
      "        (2): ReLU()\n",
      "        (3): ConstantPad1d(padding=(0, 0), value=0)\n",
      "        (4): Conv1d(6, 6, kernel_size=(1,), stride=(1,), dilation=(2,))\n",
      "        (5): ReLU()\n",
      "        (6): Dropout(p=0, inplace=False)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (classifier): Conv1d(6, 6, kernel_size=(1,), stride=(1,))\n",
      ")\n",
      "=================================================================\n",
      "Layer (type:depth-idx)                   Param #\n",
      "=================================================================\n",
      "TCN                                      --\n",
      "├─Sequential: 1-1                        --\n",
      "│    └─CausalConv1dBlock: 2-1            --\n",
      "│    │    └─Sequential: 3-1              84\n",
      "│    └─CausalConv1dBlock: 2-2            --\n",
      "│    │    └─Sequential: 3-2              84\n",
      "├─Conv1d: 1-2                            42\n",
      "=================================================================\n",
      "Total params: 210\n",
      "Trainable params: 210\n",
      "Non-trainable params: 0\n",
      "=================================================================\n",
      "\n",
      "****** TCN(b=2,k=2,d=0.0) ******\n",
      "TCN(\n",
      "  (network): Sequential(\n",
      "    (0): CausalConv1dBlock(\n",
      "      (network): Sequential(\n",
      "        (0): ConstantPad1d(padding=(1, 0), value=0)\n",
      "        (1): Conv1d(6, 6, kernel_size=(2,), stride=(1,))\n",
      "        (2): ReLU()\n",
      "        (3): ConstantPad1d(padding=(1, 0), value=0)\n",
      "        (4): Conv1d(6, 6, kernel_size=(2,), stride=(1,))\n",
      "        (5): ReLU()\n",
      "        (6): Dropout(p=0, inplace=False)\n",
      "      )\n",
      "    )\n",
      "    (1): CausalConv1dBlock(\n",
      "      (network): Sequential(\n",
      "        (0): ConstantPad1d(padding=(2, 0), value=0)\n",
      "        (1): Conv1d(6, 6, kernel_size=(2,), stride=(1,), dilation=(2,))\n",
      "        (2): ReLU()\n",
      "        (3): ConstantPad1d(padding=(2, 0), value=0)\n",
      "        (4): Conv1d(6, 6, kernel_size=(2,), stride=(1,), dilation=(2,))\n",
      "        (5): ReLU()\n",
      "        (6): Dropout(p=0, inplace=False)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (classifier): Conv1d(6, 6, kernel_size=(1,), stride=(1,))\n",
      ")\n",
      "=================================================================\n",
      "Layer (type:depth-idx)                   Param #\n",
      "=================================================================\n",
      "TCN                                      --\n",
      "├─Sequential: 1-1                        --\n",
      "│    └─CausalConv1dBlock: 2-1            --\n",
      "│    │    └─Sequential: 3-1              156\n",
      "│    └─CausalConv1dBlock: 2-2            --\n",
      "│    │    └─Sequential: 3-2              156\n",
      "├─Conv1d: 1-2                            42\n",
      "=================================================================\n",
      "Total params: 354\n",
      "Trainable params: 354\n",
      "Non-trainable params: 0\n",
      "=================================================================\n",
      "\n",
      "****** TCN(b=1,k=1,d=0.5) ******\n",
      "TCN(\n",
      "  (network): Sequential(\n",
      "    (0): CausalConv1dBlock(\n",
      "      (network): Sequential(\n",
      "        (0): ConstantPad1d(padding=(0, 0), value=0)\n",
      "        (1): Conv1d(6, 6, kernel_size=(1,), stride=(1,))\n",
      "        (2): ReLU()\n",
      "        (3): ConstantPad1d(padding=(0, 0), value=0)\n",
      "        (4): Conv1d(6, 6, kernel_size=(1,), stride=(1,))\n",
      "        (5): ReLU()\n",
      "        (6): Dropout(p=0.5, inplace=False)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (classifier): Conv1d(6, 6, kernel_size=(1,), stride=(1,))\n",
      ")\n",
      "=================================================================\n",
      "Layer (type:depth-idx)                   Param #\n",
      "=================================================================\n",
      "TCN                                      --\n",
      "├─Sequential: 1-1                        --\n",
      "│    └─CausalConv1dBlock: 2-1            --\n",
      "│    │    └─Sequential: 3-1              84\n",
      "├─Conv1d: 1-2                            42\n",
      "=================================================================\n",
      "Total params: 126\n",
      "Trainable params: 126\n",
      "Non-trainable params: 0\n",
      "=================================================================\n",
      "\n",
      "****** TCN(b=1,k=2,d=0.5) ******\n",
      "TCN(\n",
      "  (network): Sequential(\n",
      "    (0): CausalConv1dBlock(\n",
      "      (network): Sequential(\n",
      "        (0): ConstantPad1d(padding=(1, 0), value=0)\n",
      "        (1): Conv1d(6, 6, kernel_size=(2,), stride=(1,))\n",
      "        (2): ReLU()\n",
      "        (3): ConstantPad1d(padding=(1, 0), value=0)\n",
      "        (4): Conv1d(6, 6, kernel_size=(2,), stride=(1,))\n",
      "        (5): ReLU()\n",
      "        (6): Dropout(p=0.5, inplace=False)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (classifier): Conv1d(6, 6, kernel_size=(1,), stride=(1,))\n",
      ")\n",
      "=================================================================\n",
      "Layer (type:depth-idx)                   Param #\n",
      "=================================================================\n",
      "TCN                                      --\n",
      "├─Sequential: 1-1                        --\n",
      "│    └─CausalConv1dBlock: 2-1            --\n",
      "│    │    └─Sequential: 3-1              156\n",
      "├─Conv1d: 1-2                            42\n",
      "=================================================================\n",
      "Total params: 198\n",
      "Trainable params: 198\n",
      "Non-trainable params: 0\n",
      "=================================================================\n",
      "\n",
      "****** TCN(b=2,k=1,d=0.5) ******\n",
      "TCN(\n",
      "  (network): Sequential(\n",
      "    (0): CausalConv1dBlock(\n",
      "      (network): Sequential(\n",
      "        (0): ConstantPad1d(padding=(0, 0), value=0)\n",
      "        (1): Conv1d(6, 6, kernel_size=(1,), stride=(1,))\n",
      "        (2): ReLU()\n",
      "        (3): ConstantPad1d(padding=(0, 0), value=0)\n",
      "        (4): Conv1d(6, 6, kernel_size=(1,), stride=(1,))\n",
      "        (5): ReLU()\n",
      "        (6): Dropout(p=0.5, inplace=False)\n",
      "      )\n",
      "    )\n",
      "    (1): CausalConv1dBlock(\n",
      "      (network): Sequential(\n",
      "        (0): ConstantPad1d(padding=(0, 0), value=0)\n",
      "        (1): Conv1d(6, 6, kernel_size=(1,), stride=(1,), dilation=(2,))\n",
      "        (2): ReLU()\n",
      "        (3): ConstantPad1d(padding=(0, 0), value=0)\n",
      "        (4): Conv1d(6, 6, kernel_size=(1,), stride=(1,), dilation=(2,))\n",
      "        (5): ReLU()\n",
      "        (6): Dropout(p=0.5, inplace=False)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (classifier): Conv1d(6, 6, kernel_size=(1,), stride=(1,))\n",
      ")\n",
      "=================================================================\n",
      "Layer (type:depth-idx)                   Param #\n",
      "=================================================================\n",
      "TCN                                      --\n",
      "├─Sequential: 1-1                        --\n",
      "│    └─CausalConv1dBlock: 2-1            --\n",
      "│    │    └─Sequential: 3-1              84\n",
      "│    └─CausalConv1dBlock: 2-2            --\n",
      "│    │    └─Sequential: 3-2              84\n",
      "├─Conv1d: 1-2                            42\n",
      "=================================================================\n",
      "Total params: 210\n",
      "Trainable params: 210\n",
      "Non-trainable params: 0\n",
      "=================================================================\n",
      "\n",
      "****** TCN(b=2,k=2,d=0.5) ******\n",
      "TCN(\n",
      "  (network): Sequential(\n",
      "    (0): CausalConv1dBlock(\n",
      "      (network): Sequential(\n",
      "        (0): ConstantPad1d(padding=(1, 0), value=0)\n",
      "        (1): Conv1d(6, 6, kernel_size=(2,), stride=(1,))\n",
      "        (2): ReLU()\n",
      "        (3): ConstantPad1d(padding=(1, 0), value=0)\n",
      "        (4): Conv1d(6, 6, kernel_size=(2,), stride=(1,))\n",
      "        (5): ReLU()\n",
      "        (6): Dropout(p=0.5, inplace=False)\n",
      "      )\n",
      "    )\n",
      "    (1): CausalConv1dBlock(\n",
      "      (network): Sequential(\n",
      "        (0): ConstantPad1d(padding=(2, 0), value=0)\n",
      "        (1): Conv1d(6, 6, kernel_size=(2,), stride=(1,), dilation=(2,))\n",
      "        (2): ReLU()\n",
      "        (3): ConstantPad1d(padding=(2, 0), value=0)\n",
      "        (4): Conv1d(6, 6, kernel_size=(2,), stride=(1,), dilation=(2,))\n",
      "        (5): ReLU()\n",
      "        (6): Dropout(p=0.5, inplace=False)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (classifier): Conv1d(6, 6, kernel_size=(1,), stride=(1,))\n",
      ")\n",
      "=================================================================\n",
      "Layer (type:depth-idx)                   Param #\n",
      "=================================================================\n",
      "TCN                                      --\n",
      "├─Sequential: 1-1                        --\n",
      "│    └─CausalConv1dBlock: 2-1            --\n",
      "│    │    └─Sequential: 3-1              156\n",
      "│    └─CausalConv1dBlock: 2-2            --\n",
      "│    │    └─Sequential: 3-2              156\n",
      "├─Conv1d: 1-2                            42\n",
      "=================================================================\n",
      "Total params: 354\n",
      "Trainable params: 354\n",
      "Non-trainable params: 0\n",
      "=================================================================\n"
     ]
    }
   ],
   "source": [
    "for block, kernel_size, dropout in [[1,1,0], [1,2,0], [2,1,0], [2,2,0],\n",
    "                                    [1,1,0.5], [1,2,0.5], [2,1,0.5], [2,2,0.5]]:\n",
    "    modelname = f\"TCN(b={block},k={kernel_size},d={dropout:.1f})\"\n",
    "    train_loss[modelname] = []\n",
    "\n",
    "    # Set model, optimizer, loss function and learning rate scheduler\n",
    "    model = TCN(n_features=n_features, \n",
    "                blocks=[n_features]*block,\n",
    "                kernel_size=kernel_size,\n",
    "                dropout=dropout).to(device)\n",
    "    print()\n",
    "    print('******', modelname, '******')\n",
    "    print(model)\n",
    "    print(torchinfo.summary(model))\n",
    "\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
    "    scheduler = torch.optim.lr_scheduler.StepLR(\n",
    "        optimizer, gamma=0.1, step_size=step_size\n",
    "    )\n",
    "    loss_function = nn.MSELoss()\n",
    "\n",
    "    # Run training loop over num_epochs with batch_size\n",
    "    num_epochs = step_size * num_lr\n",
    "    for epoch in range(num_epochs):\n",
    "\n",
    "        # shuffle indxs into batches\n",
    "        idxs = np.arange(len(train_exs))\n",
    "        random.shuffle(idxs)\n",
    "        batches = [idxs[i:min(len(idxs), i + batch_size)]\n",
    "                   for i in range(0, len(idxs), batch_size)]\n",
    "\n",
    "        # train by batch\n",
    "        total_loss = 0.0\n",
    "        model.train()\n",
    "        for batch in batches:\n",
    "            # input has shape (batch_size, n_features, seq_len)\n",
    "            # Creating a tensor from a list of numpy.ndarrays is extremely slow.\n",
    "            nparray = np.array([[train_exs[idx][seq] for idx in batch]\n",
    "                                for seq in range(seq_len+1)])\n",
    "            train_ex = torch.tensor(nparray).permute(1, 2, 0).float().to(device)\n",
    "            model.zero_grad()\n",
    "            X = train_ex[:,:,:-1]\n",
    "            Y = train_ex[:,:,1:]\n",
    "            output = model(X)\n",
    "            loss = loss_function(output, Y)  # calculated over all outputs\n",
    "            total_loss += float(loss) / len(batches)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            scheduler.step()\n",
    "\n",
    "        model.eval()\n",
    "        train_loss[modelname].append(total_loss)\n",
    "        if VERBOSE and (epoch % (step_size//2)) == 0:\n",
    "            print(epoch, num_epochs, optimizer.param_groups[0]['lr'], total_loss)\n",
    "\n",
    "        # Compute MSE of one-period ahead forecast error in train and test sets\n",
    "        X = torch.tensor(scaled_data.values.T)[None,:,:].float().to(device)\n",
    "        pred = model(X).cpu().detach().numpy()[0,:,:].T\n",
    "        results[modelname] = {\n",
    "            'Train Error': mean_squared_error(scaled_data.values[1:ntrain],\n",
    "                                              pred[:ntrain-1]),\n",
    "            'Test Error': mean_squared_error(scaled_data.values[ntrain:],\n",
    "                                             pred[ntrain-1:-1])\n",
    "        }\n",
    "        #print('Blocks:', block, 'Kernel size:', kernel_size, results[modelname])\n",
    "        #print(pd.concat(res, axis=1).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "734baeb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "DataFrame(train_loss).plot(ax=ax)\n",
    "ax.set_ylim(top=1.0)\n",
    "ax.set_title(f\"TCN Models Training Loss by Epoch\")\n",
    "ax.set_xlabel('Epoch')\n",
    "ax.legend(title='Model Size (blocks, kernel)')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6722d9cd",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sorted by Test Error\n"
     ]
    },
    {
     "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>Train Error</th>\n",
       "      <th>Test Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TCN(b=2,k=1,d=0.5)</th>\n",
       "      <td>0.730975</td>\n",
       "      <td>0.797471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1D-Convolution</th>\n",
       "      <td>0.734049</td>\n",
       "      <td>0.804834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCN(b=1,k=1,d=0.0)</th>\n",
       "      <td>0.730197</td>\n",
       "      <td>0.823324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCN(b=2,k=2,d=0.5)</th>\n",
       "      <td>0.703123</td>\n",
       "      <td>0.825678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCN(b=1,k=2,d=0.5)</th>\n",
       "      <td>0.716962</td>\n",
       "      <td>0.852393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCN(b=1,k=1,d=0.5)</th>\n",
       "      <td>0.735571</td>\n",
       "      <td>0.865943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCN(b=1,k=2,d=0.0)</th>\n",
       "      <td>0.708393</td>\n",
       "      <td>0.877891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCN(b=2,k=1,d=0.0)</th>\n",
       "      <td>0.726959</td>\n",
       "      <td>0.881181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCN(b=2,k=2,d=0.0)</th>\n",
       "      <td>0.706048</td>\n",
       "      <td>0.932908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Train Error  Test Error\n",
       "TCN(b=2,k=1,d=0.5)     0.730975    0.797471\n",
       "1D-Convolution         0.734049    0.804834\n",
       "TCN(b=1,k=1,d=0.0)     0.730197    0.823324\n",
       "TCN(b=2,k=2,d=0.5)     0.703123    0.825678\n",
       "TCN(b=1,k=2,d=0.5)     0.716962    0.852393\n",
       "TCN(b=1,k=1,d=0.5)     0.735571    0.865943\n",
       "TCN(b=1,k=2,d=0.0)     0.708393    0.877891\n",
       "TCN(b=2,k=1,d=0.0)     0.726959    0.881181\n",
       "TCN(b=2,k=2,d=0.0)     0.706048    0.932908"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Sorted by Test Error')\n",
    "DataFrame(results).T.sort_values('Test Error')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81789045",
   "metadata": {},
   "source": [
    "## Vector Autoregression\n",
    "\n",
    "predict multiple time series (of the extracted approximate factors)\n",
    "- vector autoregression as a weighted moving average\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e529f22a",
   "metadata": {},
   "outputs": [],
   "source": [
    "var_model = VAR(scaled_data.iloc[:ntrain], freq='ME')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cb63afa",
   "metadata": {},
   "source": [
    "### Lag order\n",
    "\n",
    "The lagged coefficients estimated from the Vector Autoregression produce a \n",
    "multi-period cumulative forecast \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f65c43d1",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of VAR(p) lags selected by various IC\n"
     ]
    },
    {
     "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>IC:</th>\n",
       "      <th>aic</th>\n",
       "      <th>fpe</th>\n",
       "      <th>hqic</th>\n",
       "      <th>bic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>optimal p:</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "IC:         aic  fpe  hqic  bic\n",
       "optimal p:    3    3     2    2"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Optimal number of VAR(p) lags selected by various IC\")\n",
    "DataFrame({ic: var_model.fit(maxlags=maxlags, ic=ic).k_ar\n",
    "           for ic in ['aic', 'fpe', 'hqic', 'bic']},\n",
    "          index=['optimal p:'])\\\n",
    "          .rename_axis(columns='IC:')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "38064c98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit VAR(p) models\n",
    "var_models = {p: var_model.fit(p) for p in range(1, maxlags+1)} # fit models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d40bcbdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Summary of Regression Results   \n",
      "==================================\n",
      "Model:                         VAR\n",
      "Method:                        OLS\n",
      "Date:           Mon, 27, May, 2024\n",
      "Time:                     14:50:03\n",
      "--------------------------------------------------------------------\n",
      "No. of Equations:         6.00000    BIC:                   -1.69952\n",
      "Nobs:                     657.000    HQIC:                  -1.87519\n",
      "Log likelihood:          -4898.92    FPE:                   0.137188\n",
      "AIC:                     -1.98641    Det(Omega_mle):        0.128736\n",
      "--------------------------------------------------------------------\n",
      "Results for equation Food and Beverages\n",
      "==============================================================================================\n",
      "                                 coefficient       std. error           t-stat            prob\n",
      "----------------------------------------------------------------------------------------------\n",
      "const                              -0.002494         0.035539           -0.070           0.944\n",
      "L1.Food and Beverages               0.310812         0.037875            8.206           0.000\n",
      "L1.Housing                          0.213124         0.043391            4.912           0.000\n",
      "L1.Apparel                         -0.003392         0.037857           -0.090           0.929\n",
      "L1.Transportation                  -0.005201         0.037952           -0.137           0.891\n",
      "L1.Medical Care                    -0.007755         0.042442           -0.183           0.855\n",
      "L1.Other Goods and Services         0.009024         0.037218            0.242           0.808\n",
      "==============================================================================================\n",
      "\n",
      "Results for equation Housing\n",
      "==============================================================================================\n",
      "                                 coefficient       std. error           t-stat            prob\n",
      "----------------------------------------------------------------------------------------------\n",
      "const                              -0.013239         0.027721           -0.478           0.633\n",
      "L1.Food and Beverages               0.163946         0.029543            5.549           0.000\n",
      "L1.Housing                          0.494461         0.033845           14.609           0.000\n",
      "L1.Apparel                          0.038379         0.029529            1.300           0.194\n",
      "L1.Transportation                   0.072678         0.029603            2.455           0.014\n",
      "L1.Medical Care                     0.209727         0.033105            6.335           0.000\n",
      "L1.Other Goods and Services        -0.022371         0.029031           -0.771           0.441\n",
      "==============================================================================================\n",
      "\n",
      "Results for equation Apparel\n",
      "==============================================================================================\n",
      "                                 coefficient       std. error           t-stat            prob\n",
      "----------------------------------------------------------------------------------------------\n",
      "const                              -0.007941         0.036635           -0.217           0.828\n",
      "L1.Food and Beverages               0.031403         0.039043            0.804           0.421\n",
      "L1.Housing                          0.110336         0.044729            2.467           0.014\n",
      "L1.Apparel                          0.139551         0.039025            3.576           0.000\n",
      "L1.Transportation                   0.186002         0.039123            4.754           0.000\n",
      "L1.Medical Care                     0.117097         0.043751            2.676           0.007\n",
      "L1.Other Goods and Services         0.007189         0.038366            0.187           0.851\n",
      "==============================================================================================\n",
      "\n",
      "Results for equation Transportation\n",
      "==============================================================================================\n",
      "                                 coefficient       std. error           t-stat            prob\n",
      "----------------------------------------------------------------------------------------------\n",
      "const                               0.000177         0.034475            0.005           0.996\n",
      "L1.Food and Beverages               0.020122         0.036742            0.548           0.584\n",
      "L1.Housing                          0.072483         0.042092            1.722           0.085\n",
      "L1.Apparel                          0.036200         0.036724            0.986           0.324\n",
      "L1.Transportation                   0.422621         0.036816           11.479           0.000\n",
      "L1.Medical Care                     0.048914         0.041172            1.188           0.235\n",
      "L1.Other Goods and Services        -0.033877         0.036105           -0.938           0.348\n",
      "==============================================================================================\n",
      "\n",
      "Results for equation Medical Care\n",
      "==============================================================================================\n",
      "                                 coefficient       std. error           t-stat            prob\n",
      "----------------------------------------------------------------------------------------------\n",
      "const                               0.027613         0.029051            0.951           0.342\n",
      "L1.Food and Beverages               0.024938         0.030960            0.805           0.421\n",
      "L1.Housing                          0.234873         0.035469            6.622           0.000\n",
      "L1.Apparel                          0.043036         0.030946            1.391           0.164\n",
      "L1.Transportation                   0.004867         0.031023            0.157           0.875\n",
      "L1.Medical Care                     0.431491         0.034694           12.437           0.000\n",
      "L1.Other Goods and Services         0.115051         0.030424            3.782           0.000\n",
      "==============================================================================================\n",
      "\n",
      "Results for equation Other Goods and Services\n",
      "==============================================================================================\n",
      "                                 coefficient       std. error           t-stat            prob\n",
      "----------------------------------------------------------------------------------------------\n",
      "const                              -0.010534         0.037357           -0.282           0.778\n",
      "L1.Food and Beverages               0.026800         0.039813            0.673           0.501\n",
      "L1.Housing                          0.077605         0.045611            1.701           0.089\n",
      "L1.Apparel                          0.095652         0.039795            2.404           0.016\n",
      "L1.Transportation                   0.017545         0.039894            0.440           0.660\n",
      "L1.Medical Care                     0.257502         0.044614            5.772           0.000\n",
      "L1.Other Goods and Services         0.011147         0.039123            0.285           0.776\n",
      "==============================================================================================\n",
      "\n",
      "Correlation matrix of residuals\n",
      "                            Food and Beverages   Housing   Apparel  Transportation  Medical Care  Other Goods and Services\n",
      "Food and Beverages                    1.000000  0.147115  0.084629       -0.001656      0.014996                 -0.008149\n",
      "Housing                               0.147115  1.000000  0.091234        0.132240      0.095471                  0.019458\n",
      "Apparel                               0.084629  0.091234  1.000000        0.128269      0.036681                  0.024285\n",
      "Transportation                       -0.001656  0.132240  0.128269        1.000000     -0.053153                 -0.013329\n",
      "Medical Care                          0.014996  0.095471  0.036681       -0.053153      1.000000                  0.153789\n",
      "Other Goods and Services             -0.008149  0.019458  0.024285       -0.013329      0.153789                  1.000000\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Show model summary for VAR(1)\n",
    "print(var_models[1].summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92349603",
   "metadata": {},
   "source": [
    "Compare VAR(1) model coefficients to fitted weights of 1D convolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f065ff19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients of VAR(1) model\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Food and Beverages</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Apparel</th>\n",
       "      <th>Transportation</th>\n",
       "      <th>Medical Care</th>\n",
       "      <th>Other Goods and Services</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>const</th>\n",
       "      <td>-0.0025</td>\n",
       "      <td>-0.0132</td>\n",
       "      <td>-0.0079</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0276</td>\n",
       "      <td>-0.0105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L1.Food and Beverages</th>\n",
       "      <td>0.3108</td>\n",
       "      <td>0.1639</td>\n",
       "      <td>0.0314</td>\n",
       "      <td>0.0201</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>0.0268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L1.Housing</th>\n",
       "      <td>0.2131</td>\n",
       "      <td>0.4945</td>\n",
       "      <td>0.1103</td>\n",
       "      <td>0.0725</td>\n",
       "      <td>0.2349</td>\n",
       "      <td>0.0776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L1.Apparel</th>\n",
       "      <td>-0.0034</td>\n",
       "      <td>0.0384</td>\n",
       "      <td>0.1396</td>\n",
       "      <td>0.0362</td>\n",
       "      <td>0.0430</td>\n",
       "      <td>0.0957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L1.Transportation</th>\n",
       "      <td>-0.0052</td>\n",
       "      <td>0.0727</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.4226</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.0175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L1.Medical Care</th>\n",
       "      <td>-0.0078</td>\n",
       "      <td>0.2097</td>\n",
       "      <td>0.1171</td>\n",
       "      <td>0.0489</td>\n",
       "      <td>0.4315</td>\n",
       "      <td>0.2575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L1.Other Goods and Services</th>\n",
       "      <td>0.0090</td>\n",
       "      <td>-0.0224</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>-0.0339</td>\n",
       "      <td>0.1151</td>\n",
       "      <td>0.0111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Food and Beverages  Housing  Apparel  \\\n",
       "const                                   -0.0025  -0.0132  -0.0079   \n",
       "L1.Food and Beverages                    0.3108   0.1639   0.0314   \n",
       "L1.Housing                               0.2131   0.4945   0.1103   \n",
       "L1.Apparel                              -0.0034   0.0384   0.1396   \n",
       "L1.Transportation                       -0.0052   0.0727   0.1860   \n",
       "L1.Medical Care                         -0.0078   0.2097   0.1171   \n",
       "L1.Other Goods and Services              0.0090  -0.0224   0.0072   \n",
       "\n",
       "                             Transportation  Medical Care  \\\n",
       "const                                0.0002        0.0276   \n",
       "L1.Food and Beverages                0.0201        0.0249   \n",
       "L1.Housing                           0.0725        0.2349   \n",
       "L1.Apparel                           0.0362        0.0430   \n",
       "L1.Transportation                    0.4226        0.0049   \n",
       "L1.Medical Care                      0.0489        0.4315   \n",
       "L1.Other Goods and Services         -0.0339        0.1151   \n",
       "\n",
       "                             Other Goods and Services  \n",
       "const                                         -0.0105  \n",
       "L1.Food and Beverages                          0.0268  \n",
       "L1.Housing                                     0.0776  \n",
       "L1.Apparel                                     0.0957  \n",
       "L1.Transportation                              0.0175  \n",
       "L1.Medical Care                                0.2575  \n",
       "L1.Other Goods and Services                    0.0111  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Coefficients of VAR(1) model')\n",
    "DataFrame(np.vstack([var_models[1].intercept, var_models[1].coefs[0].T]),\n",
    "          columns=var_models[1].names, index=var_models[1].exog_names).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ebf037cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor weights of Conv1D\n"
     ]
    },
    {
     "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>Food and Beverages</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Apparel</th>\n",
       "      <th>Transportation</th>\n",
       "      <th>Medical Care</th>\n",
       "      <th>Other Goods and Services</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bias</th>\n",
       "      <td>-0.0025</td>\n",
       "      <td>-0.0132</td>\n",
       "      <td>-0.0079</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0276</td>\n",
       "      <td>-0.0105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food and Beverages</th>\n",
       "      <td>0.3108</td>\n",
       "      <td>0.1639</td>\n",
       "      <td>0.0314</td>\n",
       "      <td>0.0201</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>0.0268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Housing</th>\n",
       "      <td>0.2131</td>\n",
       "      <td>0.4945</td>\n",
       "      <td>0.1103</td>\n",
       "      <td>0.0725</td>\n",
       "      <td>0.2349</td>\n",
       "      <td>0.0776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apparel</th>\n",
       "      <td>-0.0034</td>\n",
       "      <td>0.0384</td>\n",
       "      <td>0.1396</td>\n",
       "      <td>0.0362</td>\n",
       "      <td>0.0430</td>\n",
       "      <td>0.0957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transportation</th>\n",
       "      <td>-0.0052</td>\n",
       "      <td>0.0727</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.4226</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.0175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medical Care</th>\n",
       "      <td>-0.0078</td>\n",
       "      <td>0.2097</td>\n",
       "      <td>0.1171</td>\n",
       "      <td>0.0489</td>\n",
       "      <td>0.4315</td>\n",
       "      <td>0.2575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other Goods and Services</th>\n",
       "      <td>0.0090</td>\n",
       "      <td>-0.0224</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>-0.0339</td>\n",
       "      <td>0.1151</td>\n",
       "      <td>0.0111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Food and Beverages  Housing  Apparel  \\\n",
       "bias                                 -0.0025  -0.0132  -0.0079   \n",
       "Food and Beverages                    0.3108   0.1639   0.0314   \n",
       "Housing                               0.2131   0.4945   0.1103   \n",
       "Apparel                              -0.0034   0.0384   0.1396   \n",
       "Transportation                       -0.0052   0.0727   0.1860   \n",
       "Medical Care                         -0.0078   0.2097   0.1171   \n",
       "Other Goods and Services              0.0090  -0.0224   0.0072   \n",
       "\n",
       "                          Transportation  Medical Care  \\\n",
       "bias                              0.0002        0.0276   \n",
       "Food and Beverages                0.0201        0.0249   \n",
       "Housing                           0.0725        0.2349   \n",
       "Apparel                           0.0362        0.0430   \n",
       "Transportation                    0.4226        0.0049   \n",
       "Medical Care                      0.0489        0.4315   \n",
       "Other Goods and Services         -0.0339        0.1151   \n",
       "\n",
       "                          Other Goods and Services  \n",
       "bias                                       -0.0105  \n",
       "Food and Beverages                          0.0268  \n",
       "Housing                                     0.0776  \n",
       "Apparel                                     0.0957  \n",
       "Transportation                              0.0175  \n",
       "Medical Care                                0.2575  \n",
       "Other Goods and Services                    0.0111  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Tensor weights of Conv1D')\n",
    "DataFrame(conv1d_weights, columns=names, index=['bias'] + names).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6eb1b4b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate forecast errors for each observation and model\n",
    "test_errors = {p: list() for p in range(maxlags+1)}\n",
    "train_errors = {p: list() for p in range(maxlags+1)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8a73086d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(maxlags, len(scaled_data)-1):\n",
    "    data = scaled_data.iloc[i].values\n",
    "\n",
    "    # test or train sample\n",
    "    var_errors = train_errors if i < ntrain else test_errors\n",
    "    \n",
    "    # error of unconditional mean forecast\n",
    "    var_errors[0].append(mean_squared_error(data, scaled_data.iloc[:ntrain].mean()))\n",
    "\n",
    "    # accumulate to error of VAR(p) model forecasts\n",
    "    for p in range(1, maxlags+1):\n",
    "        pred = var_models[p].forecast(scaled_data.iloc[:i].values, 1)\n",
    "        var_errors[p].append(mean_squared_error(data.reshape(1, -1), pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "74285de5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VAR models train and test set errors\n"
     ]
    },
    {
     "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>Train Error</th>\n",
       "      <th>Test Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>VAR(0)</th>\n",
       "      <td>1.005237</td>\n",
       "      <td>1.000794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VAR(1)</th>\n",
       "      <td>0.736693</td>\n",
       "      <td>0.807536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VAR(2)</th>\n",
       "      <td>0.694004</td>\n",
       "      <td>0.800034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VAR(3)</th>\n",
       "      <td>0.678115</td>\n",
       "      <td>0.782192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VAR(4)</th>\n",
       "      <td>0.669869</td>\n",
       "      <td>0.786281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VAR(5)</th>\n",
       "      <td>0.655099</td>\n",
       "      <td>0.781057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VAR(6)</th>\n",
       "      <td>0.645045</td>\n",
       "      <td>0.791213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Train Error  Test Error\n",
       "VAR(0)     1.005237    1.000794\n",
       "VAR(1)     0.736693    0.807536\n",
       "VAR(2)     0.694004    0.800034\n",
       "VAR(3)     0.678115    0.782192\n",
       "VAR(4)     0.669869    0.786281\n",
       "VAR(5)     0.655099    0.781057\n",
       "VAR(6)     0.645045    0.791213"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Collect mean test and train set errors of all VAR(p) models\n",
    "print('VAR models train and test set errors')\n",
    "out = DataFrame({'Train Error': {f\"VAR({p})\" : np.mean(errors)\n",
    "                                 for p, errors in train_errors.items()},\n",
    "                 'Test Error':  {f\"VAR({p})\" : np.mean(errors)\n",
    "                                 for p, errors in test_errors.items()}})\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d958e1eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Errors\n",
    "fig, ax = plt.subplots(1, 1, figsize=(10, 5))\n",
    "ax.plot(np.arange(len(out)), out['Train Error'], color=\"C0\")\n",
    "ax.plot(np.arange(len(out)), out['Test Error'], color=\"C1\")\n",
    "ax.plot([], [], color=\"C2\")   # dummy for legend labels\n",
    "argmin = out['Test Error'].argmin()\n",
    "ax.plot(argmin, out.iloc[argmin]['Test Error'], 'o', color=\"C2\")\n",
    "ax.set_title(f'Var(p) Forecast Errors')\n",
    "ax.set_xlabel('lag order of VAR(p)')\n",
    "ax.legend(['Train Error', 'Test Error',\n",
    "           f'Min Test Error = {out.iloc[argmin][\"Test Error\"]:.4f}'],\n",
    "          loc='upper right')\n",
    "plt.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "main_language": "python",
   "notebook_metadata_filter": "-all"
  },
  "kernelspec": {
   "display_name": "env3.11",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}