Exponential Smoothing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exponential Smoothing with `JAX`\n",
    "\n",
    "Consider the simple (or single) exponential smoothing model given by\n",
    "$$\n",
    "\\hat{y}_{t+1} = \\alpha y_t + (1 - \\alpha)(y_t - \\hat{y}_t),\n",
    "$$\n",
    "where $\\hat{y}_t$ is the predicted value of $y_t$ at time $t$, while $y_t$ denotes the true observed value at time $t$. Thus the prediction for the next timestep is given as a weighted combination of the last observed value and its predicted value. In this notebook, we'll fit the above model to the Kaggle air passengers dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jax\n",
    "import jax.numpy as jnp\n",
    "from jax import random\n",
    "import pandas as pd\n",
    "from collections import namedtuple\n",
    "from plotnine import ggplot, aes, geom_line, scale_x_datetime\n",
    "randkey = random.PRNGKey(0)\n",
    "ModelParams = namedtuple('ModelParams', 'alpha')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Month</th>\n",
       "      <th>#Passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1960-08-01</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1960-09-01</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1960-10-01</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1960-11-01</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1960-12-01</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Month  #Passengers\n",
       "0   1949-01-01          112\n",
       "1   1949-02-01          118\n",
       "2   1949-03-01          132\n",
       "3   1949-04-01          129\n",
       "4   1949-05-01          121\n",
       "..         ...          ...\n",
       "139 1960-08-01          606\n",
       "140 1960-09-01          508\n",
       "141 1960-10-01          461\n",
       "142 1960-11-01          390\n",
       "143 1960-12-01          432\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers = pd.read_csv(\"datasets/AirPassengers.csv\", parse_dates=['Month'])\n",
    "training_data = jnp.array(air_passengers['#Passengers'])\n",
    "air_passengers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset is typical of business/economic timeseries data, exhibiting trend and seasonality seen clearly in the chart below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 640
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure Size: (640 x 480)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(air_passengers) + aes(x=\"Month\", y=\"#Passengers\") + scale_x_datetime(date_labels=\"%Y-%m\")  + geom_line()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the model itself is defined, along with a mean-squared error loss function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "@jax.jit\n",
    "def predict_single(params: ModelParams, y, y_hat):\n",
    "    return params.alpha * y + (1.0 - params.alpha) * y_hat\n",
    "\n",
    "@jax.jit\n",
    "def predict(params: ModelParams, ys: jnp.array):\n",
    "    predictions = [ys[0]]\n",
    "    for k in range(0, len(ys)):\n",
    "        y_hat = predict_single(params, ys[k], predictions[k])\n",
    "        predictions.append(y_hat)\n",
    "    return jnp.array(predictions)\n",
    "\n",
    "@jax.jit\n",
    "def loss(params: ModelParams, ys: jnp.array):\n",
    "    preds = predict(params, ys)[:-1]\n",
    "    return jnp.square(preds - ys).mean()\n",
    "\n",
    "loss_grad = jax.grad(loss)\n",
    "params = ModelParams(0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the uncalibrated model's predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 640
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure Size: (640 x 480)>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers['preds'] = predict(params, training_data)[:-1]\n",
    "ggplot(air_passengers) + aes(x=\"Month\", y=\"preds\") + scale_x_datetime(date_labels=\"%Y-%m\") + geom_line()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training proceeds as usual: minimize loss via gradient descent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss 2.7e+03\n",
      "alpha is now 0.1\n",
      "loss 9.9e+02\n",
      "alpha is now 1.3739\n",
      "loss 9.9e+02\n",
      "alpha is now 1.4016\n",
      "loss 9.9e+02\n",
      "alpha is now 1.4058\n",
      "loss 9.9e+02\n",
      "alpha is now 1.4065\n",
      "loss 9.9e+02\n",
      "alpha is now 1.4066\n"
     ]
    }
   ],
   "source": [
    "eta = 1e-4\n",
    "for i in range(10_000):\n",
    "    if i % 10 == 0:\n",
    "        ell = loss(params, training_data)\n",
    "        print(f\"loss {ell:.2}\")\n",
    "        print(f\"alpha is now {params.alpha:.5}\")\n",
    "    G = loss_grad(params, training_data)\n",
    "    old_params = params\n",
    "    params = ModelParams(params.alpha - eta * G.alpha)\n",
    "    if abs(old_params.alpha - params.alpha) < 1e-6:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how it looks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 640
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure Size: (640 x 480)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers['preds'] = predict(params, training_data)[:-1]\n",
    "ggplot(air_passengers) + aes(x=\"Month\", y=\"preds\") + scale_x_datetime(date_labels=\"%Y-%m\") + geom_line()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use `statsmodels` and fit a baseline simple exponential smoothing model to this data for comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SimpleExpSmoothing Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>#Passengers</td>    <th>  No. Observations:  </th>        <td>144</td>      \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>SimpleExpSmoothing</td> <th>  SSE                </th>    <td>163003.353</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Optimized:</th>               <td>True</td>        <th>  AIC                </th>     <td>1016.567</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Trend:</th>                   <td>None</td>        <th>  BIC                </th>     <td>1022.506</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Seasonal:</th>                <td>None</td>        <th>  AICC               </th>     <td>1016.854</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Seasonal Periods:</th>        <td>None</td>        <th>  Date:              </th> <td>Mon, 14 Aug 2023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Box-Cox:</th>                 <td>False</td>       <th>  Time:              </th>     <td>09:38:55</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Box-Cox Coeff.:</th>          <td>None</td>        <th>                     </th>         <td> </td>       \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>                 <th>coeff</th>                <th>code</th>               <th>optimized</th>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>smoothing_level</th> <td>           0.9950000</td> <td>               alpha</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_level</th>   <td>           112.00000</td> <td>                 l.0</td> <td>               False</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     \\#Passengers     & \\textbf{  No. Observations:  } &         144           \\\\\n",
       "\\textbf{Model:}            &  SimpleExpSmoothing  & \\textbf{  SSE                } &      163003.353       \\\\\n",
       "\\textbf{Optimized:}        &         True         & \\textbf{  AIC                } &       1016.567        \\\\\n",
       "\\textbf{Trend:}            &         None         & \\textbf{  BIC                } &       1022.506        \\\\\n",
       "\\textbf{Seasonal:}         &         None         & \\textbf{  AICC               } &       1016.854        \\\\\n",
       "\\textbf{Seasonal Periods:} &         None         & \\textbf{  Date:              } &   Mon, 14 Aug 2023    \\\\\n",
       "\\textbf{Box-Cox:}          &        False         & \\textbf{  Time:              } &       09:38:55        \\\\\n",
       "\\textbf{Box-Cox Coeff.:}   &         None         & \\textbf{                     } &                       \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lccc}\n",
       "                          &    \\textbf{coeff}    &    \\textbf{code}     &  \\textbf{optimized}   \\\\\n",
       "\\midrule\n",
       "\\textbf{smoothing\\_level} &            0.9950000 &                alpha &                 True  \\\\\n",
       "\\textbf{initial\\_level}   &            112.00000 &                  l.0 &                False  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{SimpleExpSmoothing Model Results}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                       SimpleExpSmoothing Model Results                       \n",
       "==============================================================================\n",
       "Dep. Variable:            #Passengers   No. Observations:                  144\n",
       "Model:             SimpleExpSmoothing   SSE                         163003.353\n",
       "Optimized:                       True   AIC                           1016.567\n",
       "Trend:                           None   BIC                           1022.506\n",
       "Seasonal:                        None   AICC                          1016.854\n",
       "Seasonal Periods:                None   Date:                 Mon, 14 Aug 2023\n",
       "Box-Cox:                        False   Time:                         09:38:55\n",
       "Box-Cox Coeff.:                  None                                         \n",
       "==============================================================================\n",
       "                       coeff                 code              optimized      \n",
       "------------------------------------------------------------------------------\n",
       "smoothing_level            0.9950000                alpha                 True\n",
       "initial_level              112.00000                  l.0                False\n",
       "------------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.holtwinters import SimpleExpSmoothing\n",
    "res = SimpleExpSmoothing(air_passengers['#Passengers']).fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`statsmodels` has chosen a very different $\\alpha$ coefficient (here called `smoothing_level`). Is our result bad? We can set the `smoothing_level` to our model's value and see what the scorecard says. Pay attention to the AIC in the results below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sca/kaggle/.venv/lib/python3.10/site-packages/pandas/util/_decorators.py:210: EstimationWarning: Model has no free parameters to estimate. Set optimized=False to suppress this warning\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SimpleExpSmoothing Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>#Passengers</td>    <th>  No. Observations:  </th>        <td>144</td>      \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>SimpleExpSmoothing</td> <th>  SSE                </th>    <td>142454.663</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Optimized:</th>               <td>False</td>       <th>  AIC                </th>      <td>997.163</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Trend:</th>                   <td>None</td>        <th>  BIC                </th>     <td>1003.103</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Seasonal:</th>                <td>None</td>        <th>  AICC               </th>      <td>997.451</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Seasonal Periods:</th>        <td>None</td>        <th>  Date:              </th> <td>Mon, 14 Aug 2023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Box-Cox:</th>                 <td>False</td>       <th>  Time:              </th>     <td>09:38:55</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Box-Cox Coeff.:</th>          <td>None</td>        <th>                     </th>         <td> </td>       \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>                 <th>coeff</th>                <th>code</th>               <th>optimized</th>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>smoothing_level</th> <td>           1.4065973</td> <td>               alpha</td> <td>               False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_level</th>   <td>           112.00000</td> <td>                 l.0</td> <td>               False</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     \\#Passengers     & \\textbf{  No. Observations:  } &         144           \\\\\n",
       "\\textbf{Model:}            &  SimpleExpSmoothing  & \\textbf{  SSE                } &      142454.663       \\\\\n",
       "\\textbf{Optimized:}        &        False         & \\textbf{  AIC                } &       997.163         \\\\\n",
       "\\textbf{Trend:}            &         None         & \\textbf{  BIC                } &       1003.103        \\\\\n",
       "\\textbf{Seasonal:}         &         None         & \\textbf{  AICC               } &       997.451         \\\\\n",
       "\\textbf{Seasonal Periods:} &         None         & \\textbf{  Date:              } &   Mon, 14 Aug 2023    \\\\\n",
       "\\textbf{Box-Cox:}          &        False         & \\textbf{  Time:              } &       09:38:55        \\\\\n",
       "\\textbf{Box-Cox Coeff.:}   &         None         & \\textbf{                     } &                       \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lccc}\n",
       "                          &    \\textbf{coeff}    &    \\textbf{code}     &  \\textbf{optimized}   \\\\\n",
       "\\midrule\n",
       "\\textbf{smoothing\\_level} &            1.4065973 &                alpha &                False  \\\\\n",
       "\\textbf{initial\\_level}   &            112.00000 &                  l.0 &                False  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{SimpleExpSmoothing Model Results}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                       SimpleExpSmoothing Model Results                       \n",
       "==============================================================================\n",
       "Dep. Variable:            #Passengers   No. Observations:                  144\n",
       "Model:             SimpleExpSmoothing   SSE                         142454.663\n",
       "Optimized:                      False   AIC                            997.163\n",
       "Trend:                           None   BIC                           1003.103\n",
       "Seasonal:                        None   AICC                           997.451\n",
       "Seasonal Periods:                None   Date:                 Mon, 14 Aug 2023\n",
       "Box-Cox:                        False   Time:                         09:38:55\n",
       "Box-Cox Coeff.:                  None                                         \n",
       "==============================================================================\n",
       "                       coeff                 code              optimized      \n",
       "------------------------------------------------------------------------------\n",
       "smoothing_level            1.4065973                alpha                False\n",
       "initial_level              112.00000                  l.0                False\n",
       "------------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = SimpleExpSmoothing(air_passengers['#Passengers']).fit(smoothing_level=params.alpha)\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observe that our model actually fits the data marginally better than the one `statsmodels` determined."
   ]
  }
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