docs/tutorials/regression_prior.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression Priors in DLT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook demonstrates usage of priors in the regression analysis. The *iclaims* data will be used in demo purpose. Examples include\n",
    "\n",
    "1. regression with default setting\n",
    "2. regression with bounded priors for regression coefficients \n",
    "\n",
    "Generally speaking, regression coefficients are more robust under full Bayesian sampling and estimation.  The default setting `estimator='stan-mcmc'` will be used in this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-28T21:49:38.157658Z",
     "start_time": "2022-04-28T21:49:35.620843Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import orbit\n",
    "from orbit.utils.dataset import load_iclaims\n",
    "from orbit.models import DLT\n",
    "from orbit.diagnostics.plot import plot_predicted_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-28T21:49:38.163035Z",
     "start_time": "2022-04-28T21:49:38.160107Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1.4.6\n"
     ]
    }
   ],
   "source": [
    "print(orbit.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "## US Weekly Initial Claims"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall the *iclaims* dataset by previous section. In order to use this data to nowcast the US unemployment claims during COVID-19 period, the dataset is extended to Jan 2021 and the [S&P 500 (^GSPC)](https://finance.yahoo.com/quote/%5EGSPC/history?period1=1264032000&period2=1611187200&interval=1wk&filter=history&frequency=1wk&includeAdjustedClose=true) and [VIX](https://finance.yahoo.com/quote/%5EVIX/history?p=%5EVIX) Index historical data are attached for the same period.\n",
    "\n",
    "The data is standardized and log-transformed for the model fitting purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-07T18:48:33.076267Z",
     "start_time": "2022-04-07T18:48:31.033862Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "week              datetime64[ns]\n",
       "claims                   float64\n",
       "trend.unemploy           float64\n",
       "trend.job                float64\n",
       "sp500                    float64\n",
       "vix                      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data\n",
    "df = load_iclaims(end_date='2021-01-03')\n",
    "df = df[['week', 'claims', 'trend.unemploy', 'trend.job', 'sp500', 'vix']]\n",
    "df = df[1:].reset_index(drop=True)\n",
    "\n",
    "date_col = 'week'\n",
    "response_col = 'claims'\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-07T18:48:33.091950Z",
     "start_time": "2022-04-07T18:48:33.079051Z"
    }
   },
   "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>week</th>\n",
       "      <th>claims</th>\n",
       "      <th>trend.unemploy</th>\n",
       "      <th>trend.job</th>\n",
       "      <th>sp500</th>\n",
       "      <th>vix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-10</td>\n",
       "      <td>13.624218</td>\n",
       "      <td>0.016351</td>\n",
       "      <td>0.181862</td>\n",
       "      <td>-0.550891</td>\n",
       "      <td>0.069878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-17</td>\n",
       "      <td>13.398741</td>\n",
       "      <td>0.032611</td>\n",
       "      <td>0.130569</td>\n",
       "      <td>-0.590640</td>\n",
       "      <td>0.491772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-24</td>\n",
       "      <td>13.137549</td>\n",
       "      <td>-0.000179</td>\n",
       "      <td>0.119987</td>\n",
       "      <td>-0.607162</td>\n",
       "      <td>0.388078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>13.196760</td>\n",
       "      <td>-0.069172</td>\n",
       "      <td>0.087552</td>\n",
       "      <td>-0.614339</td>\n",
       "      <td>0.446838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-02-07</td>\n",
       "      <td>13.146984</td>\n",
       "      <td>-0.182500</td>\n",
       "      <td>0.019344</td>\n",
       "      <td>-0.605636</td>\n",
       "      <td>0.308205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        week     claims  trend.unemploy  trend.job     sp500       vix\n",
       "0 2010-01-10  13.624218        0.016351   0.181862 -0.550891  0.069878\n",
       "1 2010-01-17  13.398741        0.032611   0.130569 -0.590640  0.491772\n",
       "2 2010-01-24  13.137549       -0.000179   0.119987 -0.607162  0.388078\n",
       "3 2010-01-31  13.196760       -0.069172   0.087552 -0.614339  0.446838\n",
       "4 2010-02-07  13.146984       -0.182500   0.019344 -0.605636  0.308205"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see form the plot below, there are seasonality, trend, and as well as a huge change point due the impact of COVID-19."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-07T18:48:33.656211Z",
     "start_time": "2022-04-07T18:48:33.093772Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'S&P500')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2,figsize=(20,8))\n",
    "axs[0, 0].plot(df['week'], df['claims'])\n",
    "axs[0, 0].set_title('Unemployment Claims')\n",
    "axs[0, 1].plot(df['week'], df['trend.unemploy'], 'tab:orange')\n",
    "axs[0, 1].set_title('Google trend - unemploy')\n",
    "axs[1, 0].plot(df['week'], df['vix'], 'tab:green')\n",
    "axs[1, 0].set_title('VIX')\n",
    "axs[1, 1].plot(df['week'], df['sp500'], 'tab:red')\n",
    "axs[1, 1].set_title('S&P500')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-07T18:48:33.662455Z",
     "start_time": "2022-04-07T18:48:33.657947Z"
    }
   },
   "outputs": [],
   "source": [
    "# using relatively updated data\n",
    "df[['sp500']] = df[['sp500']].diff()\n",
    "df = df[1:].reset_index(drop=True)\n",
    "\n",
    "test_size = 12\n",
    "train_df = df[:-test_size]\n",
    "test_df = df[-test_size:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will use DLT models to compare the model performance with vs. without regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-07T18:48:46.681927Z",
     "start_time": "2022-04-07T18:48:33.665046Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-19 23:42:14 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 1000 and samples(per chain): 25.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 126 ms, sys: 39.3 ms, total: 165 ms\n",
      "Wall time: 11 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "dlt = DLT(\n",
    "    response_col=response_col,\n",
    "    date_col=date_col,\n",
    "    seasonality=52,\n",
    "    seed=8888,\n",
    "    num_warmup=4000,\n",
    "    stan_mcmc_args={'show_progress': False}\n",
    ")\n",
    "dlt.fit(df=train_df)\n",
    "predicted_df = dlt.predict(df=test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "### DLT With Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The regressor columns can be supplied via argument `regressor_col`.  Recall the regression formula in **DLT**:\n",
    "\n",
    "$$\n",
    "\\hat{y}_t =\\mu_t + s_t + r_t \\\\\n",
    "r_t = \\sum_{j}\\beta_j x_{jt} \\\\\n",
    "\\beta_j ~\\sim \\mathcal{N}(\\mu_j, \\sigma_j^2)\n",
    "$$\n",
    "\n",
    "By default, $\\mu_j = 0$ and $\\sigma_j = 1$.  In addition, we can set a *sign* constraint for each coefficient $\\beta_j$.  This is can be done by supplying the `regressor_sign` as a list where elements are in one of followings:\n",
    "\n",
    "* '=': $\\beta_j ~\\sim \\mathcal{N}(0, \\sigma_j^2)$  i.e. $\\beta_j \\in (-\\inf, \\inf)$\n",
    "* '+': $\\beta_j ~\\sim \\mathcal{N}^+(0, \\sigma_j^2)$  i.e. $\\beta_j \\in [0, \\inf)$\n",
    "* '-': $\\beta_j ~\\sim \\mathcal{N}^-(0, \\sigma_j^2)$  i.e. $\\beta_j \\in (-\\inf, 0]$\n",
    "\n",
    "Based on some intuition, it's reasonable to assume search terms such as \"unemployment\", \"filling\" and **VIX** index to be positively correlated and stock index such as **SP500** to be negatively correlated to the outcome.  Then we will leave whatever unspecified as a regular regressor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-07T18:49:00.202316Z",
     "start_time": "2022-04-07T18:48:46.685743Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-19 23:42:26 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 1000 and samples(per chain): 25.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 164 ms, sys: 71.3 ms, total: 235 ms\n",
      "Wall time: 13.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "dlt_reg = DLT(\n",
    "    response_col=response_col, \n",
    "    date_col=date_col,\n",
    "    regressor_col=['trend.unemploy', 'trend.job', 'sp500', 'vix'],\n",
    "    seasonality=52,\n",
    "    seed=8888,\n",
    "    num_warmup=4000,\n",
    "    stan_mcmc_args={'show_progress': False}\n",
    ")\n",
    "dlt_reg.fit(df=train_df)\n",
    "predicted_df_reg = dlt_reg.predict(test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The estimated regressor coefficients can be retrieved via `.get_regression_coefs()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-07T18:49:00.217532Z",
     "start_time": "2022-04-07T18:49:00.204563Z"
    }
   },
   "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>regressor</th>\n",
       "      <th>regressor_sign</th>\n",
       "      <th>coefficient</th>\n",
       "      <th>coefficient_lower</th>\n",
       "      <th>coefficient_upper</th>\n",
       "      <th>Pr(coef &gt;= 0)</th>\n",
       "      <th>Pr(coef &lt; 0)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>trend.unemploy</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.076715</td>\n",
       "      <td>0.042656</td>\n",
       "      <td>0.105937</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>trend.job</td>\n",
       "      <td>Regular</td>\n",
       "      <td>-0.038551</td>\n",
       "      <td>-0.081939</td>\n",
       "      <td>0.019654</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sp500</td>\n",
       "      <td>Regular</td>\n",
       "      <td>-0.001387</td>\n",
       "      <td>-0.201171</td>\n",
       "      <td>0.195193</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>vix</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.011533</td>\n",
       "      <td>-0.013659</td>\n",
       "      <td>0.035988</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        regressor regressor_sign  coefficient  coefficient_lower  \\\n",
       "0  trend.unemploy        Regular     0.076715           0.042656   \n",
       "1       trend.job        Regular    -0.038551          -0.081939   \n",
       "2           sp500        Regular    -0.001387          -0.201171   \n",
       "3             vix        Regular     0.011533          -0.013659   \n",
       "\n",
       "   coefficient_upper  Pr(coef >= 0)  Pr(coef < 0)  \n",
       "0           0.105937           1.00          0.00  \n",
       "1           0.019654           0.13          0.87  \n",
       "2           0.195193           0.49          0.51  \n",
       "3           0.035988           0.73          0.27  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dlt_reg.get_regression_coefs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression with Informative Priors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to various reasons, users may obtain further knowledge on some of the regressors or they want to propose different regularization on different regressors. These informative priors basically means to replace the defaults ($\\mu$, $\\sigma$) mentioned previously. In orbit, this process is done via the arguments `regressor_beta_prior` and `regressor_sigma_prior`. These two lists should be of the same length as `regressor_col`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition, we can set a *sign* constraint for each coefficient $\\beta_j$.  This is can be done by supplying the `regressor_sign` as a list where elements are in one of followings:\n",
    "\n",
    "* '=': $\\beta_j ~\\sim \\mathcal{N}(0, \\sigma_j^2)$  i.e. $\\beta_j \\in (-\\inf, \\inf)$\n",
    "* '+': $\\beta_j ~\\sim \\mathcal{N}^+(0, \\sigma_j^2)$  i.e. $\\beta_j \\in [0, \\inf)$\n",
    "* '-': $\\beta_j ~\\sim \\mathcal{N}^-(0, \\sigma_j^2)$  i.e. $\\beta_j \\in (-\\inf, 0]$\n",
    "\n",
    "Based on intuition, it's reasonable to assume search terms such as \"unemployment\", \"filling\" and **VIX** index to be positively correlated (`+` sign is used in this case) and upward shock of **SP500** (`-` sign) to be negatively correlated to the outcome. Otherwise, an unbounded coefficient can be used (`=` sign).\n",
    "\n",
    "Furthermore, regressors such as search queries may have more direct impact than stock marker indices.  Hence, a smaller $\\sigma$ is considered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-07T18:49:21.970562Z",
     "start_time": "2022-04-07T18:49:00.219185Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-19 23:42:39 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 1000 and samples(per chain): 250.\n"
     ]
    }
   ],
   "source": [
    "dlt_reg_adjust = DLT(\n",
    "    response_col=response_col,\n",
    "    date_col=date_col,\n",
    "    regressor_col=['trend.unemploy', 'trend.job', 'sp500','vix'],\n",
    "    regressor_sign=['+','=','-','+'],\n",
    "    regressor_sigma_prior=[0.3, 0.1, 0.05, 0.1],\n",
    "    num_warmup=4000,\n",
    "    num_sample=1000,\n",
    "    estimator='stan-mcmc',\n",
    "    seed=2022,\n",
    "    stan_mcmc_args={'show_progress': False}\n",
    ")\n",
    "dlt_reg_adjust.fit(df=train_df)\n",
    "predicted_df_reg_adjust = dlt_reg_adjust.predict(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-07T18:49:21.986924Z",
     "start_time": "2022-04-07T18:49:21.972519Z"
    }
   },
   "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>regressor</th>\n",
       "      <th>regressor_sign</th>\n",
       "      <th>coefficient</th>\n",
       "      <th>coefficient_lower</th>\n",
       "      <th>coefficient_upper</th>\n",
       "      <th>Pr(coef &gt;= 0)</th>\n",
       "      <th>Pr(coef &lt; 0)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>trend.unemploy</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.126584</td>\n",
       "      <td>0.075630</td>\n",
       "      <td>0.198016</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>vix</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.019553</td>\n",
       "      <td>0.002202</td>\n",
       "      <td>0.054368</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sp500</td>\n",
       "      <td>Negative</td>\n",
       "      <td>-0.032251</td>\n",
       "      <td>-0.087838</td>\n",
       "      <td>-0.002386</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>trend.job</td>\n",
       "      <td>Regular</td>\n",
       "      <td>-0.011294</td>\n",
       "      <td>-0.086100</td>\n",
       "      <td>0.058422</td>\n",
       "      <td>0.394</td>\n",
       "      <td>0.606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        regressor regressor_sign  coefficient  coefficient_lower  \\\n",
       "0  trend.unemploy       Positive     0.126584           0.075630   \n",
       "1             vix       Positive     0.019553           0.002202   \n",
       "2           sp500       Negative    -0.032251          -0.087838   \n",
       "3       trend.job        Regular    -0.011294          -0.086100   \n",
       "\n",
       "   coefficient_upper  Pr(coef >= 0)  Pr(coef < 0)  \n",
       "0           0.198016          1.000         0.000  \n",
       "1           0.054368          1.000         0.000  \n",
       "2          -0.002386          0.000         1.000  \n",
       "3           0.058422          0.394         0.606  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dlt_reg_adjust.get_regression_coefs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the holdout performance by using the built-in function `smape()` ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-07T18:49:21.994703Z",
     "start_time": "2022-04-07T18:49:21.988876Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------Mean Absolute Error Summary----------------\n",
      "Naive Model: 0.255\n",
      "Regression Model: 0.242\n",
      "Refined Regression Model: 0.096\n"
     ]
    }
   ],
   "source": [
    "def mae(x, y):\n",
    "    return np.mean(np.abs(x - y))\n",
    "\n",
    "naive_mae = mae(predicted_df['prediction'].values, test_df['claims'].values)\n",
    "reg_mae = mae(predicted_df_reg['prediction'].values, test_df['claims'].values)\n",
    "reg_adjust_mae = mae(predicted_df_reg_adjust['prediction'].values, test_df['claims'].values)\n",
    "\n",
    "print(\"----------------Mean Absolute Error Summary----------------\")\n",
    "print(\"Naive Model: {:.3f}\\nRegression Model: {:.3f}\\nRefined Regression Model: {:.3f}\".format(\n",
    "    naive_mae, reg_mae, reg_adjust_mae\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "This demo showcases a use case in nowcasting. Although this may not be applicable in real-time forecasting, it mainly introduces the regression analysis with time-series modeling in `Orbit`. For people who have concerns on the forecastability, one can consider introducing lag on regressors.\n",
    "\n",
    "Also, `Orbit` allows informative priors where sometime can be useful in combining multiple source of insights together."
   ]
  }
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