{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outlier Robust Forecasting"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When outliers are present in a dataset, they can disrupt the calculated summary statistics, such as the mean and standard deviation, leading the model to favor the outlier values and deviate from most observations. Consequently, models need help in achieving a balance between accurately accommodating outliers and performing well on normal data, resulting in improved overall performance on both types of data. [Robust regression algorithms](https://en.wikipedia.org/wiki/Robust_regression) tackle this issue, explicitly accounting for outliers in the dataset.\n",
    "\n",
    "In this notebook we will show how to fit robust NeuralForecast methods. We will:<br>\n",
    "- Installing NeuralForecast.<br>\n",
    "- Loading Noisy AirPassengers.<br>\n",
    "- Fit and predict robustified NeuralForecast.<br>\n",
    "- Plot and evaluate predictions.<br>\n",
    "\n",
    "You can run these experiments using GPU with Google Colab.\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/Nixtla/neuralforecast/blob/main/nbs/examples/Robust_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Installing NeuralForecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install git+https://github.com/Nixtla/neuralforecast.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from random import random\n",
    "from random import randint\n",
    "from random import seed\n",
    "\n",
    "from neuralforecast import NeuralForecast\n",
    "from neuralforecast.utils import AirPassengersDF\n",
    "\n",
    "from neuralforecast.models import NHITS\n",
    "from neuralforecast.losses.pytorch import MQLoss, DistributionLoss, HuberMQLoss\n",
    "\n",
    "from neuralforecast.losses.numpy import mape, mqloss"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Loading Noisy AirPassengers\n",
    "\n",
    "For this example we will use the classic Box-Cox AirPassengers dataset that we will augment it by introducing outliers.\n",
    "\n",
    "In particular, we will focus on introducing outliers to the target variable altering it to deviate from its original observation by a specified factor, such as 2-to-4 times the standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Original Box-Cox AirPassengers \n",
    "# as defined in neuralforecast.utils\n",
    "Y_df = AirPassengersDF.copy() \n",
    "plt.plot(Y_df.y)\n",
    "plt.ylabel('Monthly Passengers')\n",
    "plt.xlabel('Timestamp [t]')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here we add some artificial outliers to AirPassengers\n",
    "seed(1)\n",
    "for i in range(len(Y_df)):\n",
    "    factor = randint(2, 4)\n",
    "    if random() > 0.97:\n",
    "        Y_df.y[i] += factor * Y_df.y.std()\n",
    "\n",
    "plt.plot(Y_df.y)\n",
    "plt.ylabel('Monthly Passengers + Noise')\n",
    "plt.xlabel('Timestamp [t]')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>index</th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>132</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-01-31</td>\n",
       "      <td>417.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>133</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-02-29</td>\n",
       "      <td>391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>134</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-03-31</td>\n",
       "      <td>419.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>135</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-04-30</td>\n",
       "      <td>461.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>136</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-05-31</td>\n",
       "      <td>472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>137</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-06-30</td>\n",
       "      <td>535.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>138</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-07-31</td>\n",
       "      <td>622.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>139</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-08-31</td>\n",
       "      <td>606.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>140</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-09-30</td>\n",
       "      <td>508.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>141</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-10-31</td>\n",
       "      <td>461.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>142</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-11-30</td>\n",
       "      <td>390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>143</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-12-31</td>\n",
       "      <td>432.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  unique_id         ds      y\n",
       "0     132        1.0 1960-01-31  417.0\n",
       "1     133        1.0 1960-02-29  391.0\n",
       "2     134        1.0 1960-03-31  419.0\n",
       "3     135        1.0 1960-04-30  461.0\n",
       "4     136        1.0 1960-05-31  472.0\n",
       "5     137        1.0 1960-06-30  535.0\n",
       "6     138        1.0 1960-07-31  622.0\n",
       "7     139        1.0 1960-08-31  606.0\n",
       "8     140        1.0 1960-09-30  508.0\n",
       "9     141        1.0 1960-10-31  461.0\n",
       "10    142        1.0 1960-11-30  390.0\n",
       "11    143        1.0 1960-12-31  432.0"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split datasets into train/test \n",
    "# Last 12 months for test\n",
    "Y_train_df = Y_df.groupby('unique_id').head(-12).reset_index()\n",
    "Y_test_df = Y_df.groupby('unique_id').tail(12).reset_index()\n",
    "Y_test_df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Fit and predict robustified NeuralForecast"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Huber MQ Loss\n",
    "\n",
    "The Huber loss, employed in robust regression, is a loss function that \n",
    "exhibits reduced sensitivity to outliers in data when compared to the \n",
    "squared error loss. The Huber loss function is quadratic for small errors and linear for large \n",
    "errors. Here we will use a slight modification for probabilistic predictions. Feel free to play with the $\\delta$ parameter."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://github.com/Nixtla/neuralforecast/blob/main/nbs/imgs_losses/huber_loss.png?raw=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropout Regularization\n",
    "\n",
    "The dropout technique is a regularization method used in neural networks to prevent overfitting. During training, dropout randomly sets a fraction of the input units or neurons in a layer to zero at each update, effectively \"dropping out\" those units. This means that the network cannot rely on any individual unit because it may be dropped out at any time. By doing so, dropout forces the network to learn more robust and generalizable representations by preventing units from co-adapting too much.\n",
    "\n",
    "The dropout method, can help us to robustify the network to outliers in the auto-regressive features. You can explore it through the `dropout_prob_theta` parameter."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit NeuralForecast models"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `NeuralForecast.fit` method you can train a set of models to your dataset. You can define the forecasting `horizon` (12 in this example), and modify the hyperparameters of the model. For example, for the `NHITS` we changed the default hidden size for both encoder and decoders.\n",
    "\n",
    "See the `NHITS` and `MLP` [model documentation](https://nixtla.github.io/neuralforecast/models.mlp.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 1\n",
      "Global seed set to 1\n"
     ]
    }
   ],
   "source": [
    "%%capture\n",
    "horizon = 12\n",
    "quantiles = [0.1, 0.25, 0.5, 0.75, 0.9]\n",
    "\n",
    "# Try different hyperparmeters to improve accuracy.\n",
    "models = [NHITS(h=horizon,                           # Forecast horizon\n",
    "                input_size=2 * horizon,              # Length of input sequence\n",
    "                loss=HuberMQLoss(quantiles=quantiles),    # Robust Huber Loss\n",
    "                valid_loss=MQLoss(quantiles=quantiles),   # Validation signal\n",
    "                max_steps=500,                       # Number of steps to train\n",
    "                dropout_prob_theta=0.6,              # Dropout to robustify vs outlier lag inputs\n",
    "                #early_stop_patience_steps=2,        # Early stopping regularization patience\n",
    "                val_check_steps=10,                  # Frequency of validation signal (affects early stopping)\n",
    "                alias='Huber',\n",
    "              ),\n",
    "          NHITS(h=horizon,\n",
    "                input_size=2 * horizon,\n",
    "                loss=DistributionLoss(distribution='Normal', \n",
    "                                      quantiles=quantiles), # Classic Normal distribution\n",
    "                valid_loss=MQLoss(quantiles=quantiles),\n",
    "                max_steps=500,\n",
    "                #early_stop_patience_steps=2,\n",
    "                dropout_prob_theta=0.6,\n",
    "                val_check_steps=10,\n",
    "                alias='Normal',\n",
    "              )\n",
    "          ]\n",
    "nf = NeuralForecast(models=models, freq='M')\n",
    "nf.fit(df=Y_train_df)\n",
    "Y_hat_df = nf.predict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>ds</th>\n",
       "      <th>Huber-lo-80.0</th>\n",
       "      <th>Huber-lo-50.0</th>\n",
       "      <th>Huber-median</th>\n",
       "      <th>Huber-hi-50.0</th>\n",
       "      <th>Huber-hi-80.0</th>\n",
       "      <th>Normal</th>\n",
       "      <th>Normal-lo-80.0</th>\n",
       "      <th>Normal-lo-50.0</th>\n",
       "      <th>Normal-median</th>\n",
       "      <th>Normal-hi-50.0</th>\n",
       "      <th>Normal-hi-80.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1960-01-31</td>\n",
       "      <td>392.046448</td>\n",
       "      <td>397.029694</td>\n",
       "      <td>402.842377</td>\n",
       "      <td>412.451111</td>\n",
       "      <td>420.441254</td>\n",
       "      <td>370.554321</td>\n",
       "      <td>-1246.564331</td>\n",
       "      <td>-466.951233</td>\n",
       "      <td>374.260681</td>\n",
       "      <td>1214.356934</td>\n",
       "      <td>1953.910645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1960-02-29</td>\n",
       "      <td>389.217041</td>\n",
       "      <td>398.078979</td>\n",
       "      <td>411.310669</td>\n",
       "      <td>426.811432</td>\n",
       "      <td>462.116272</td>\n",
       "      <td>431.134827</td>\n",
       "      <td>-1016.146179</td>\n",
       "      <td>-376.702576</td>\n",
       "      <td>469.389313</td>\n",
       "      <td>1195.809082</td>\n",
       "      <td>1874.808838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1960-03-31</td>\n",
       "      <td>434.787323</td>\n",
       "      <td>446.318176</td>\n",
       "      <td>456.515533</td>\n",
       "      <td>468.646667</td>\n",
       "      <td>486.479950</td>\n",
       "      <td>469.221069</td>\n",
       "      <td>-965.224670</td>\n",
       "      <td>-268.812164</td>\n",
       "      <td>455.846985</td>\n",
       "      <td>1197.529175</td>\n",
       "      <td>1983.282349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1960-04-30</td>\n",
       "      <td>435.589081</td>\n",
       "      <td>443.395844</td>\n",
       "      <td>451.102966</td>\n",
       "      <td>458.542328</td>\n",
       "      <td>469.953857</td>\n",
       "      <td>544.345642</td>\n",
       "      <td>-1038.976440</td>\n",
       "      <td>-251.178711</td>\n",
       "      <td>535.880615</td>\n",
       "      <td>1403.179932</td>\n",
       "      <td>2100.239990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1960-05-31</td>\n",
       "      <td>442.144714</td>\n",
       "      <td>448.320862</td>\n",
       "      <td>455.896271</td>\n",
       "      <td>466.212524</td>\n",
       "      <td>477.713348</td>\n",
       "      <td>400.593628</td>\n",
       "      <td>-1188.452881</td>\n",
       "      <td>-417.007935</td>\n",
       "      <td>426.566284</td>\n",
       "      <td>1202.106201</td>\n",
       "      <td>2103.583008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1960-06-30</td>\n",
       "      <td>505.597168</td>\n",
       "      <td>513.204590</td>\n",
       "      <td>522.992188</td>\n",
       "      <td>535.911987</td>\n",
       "      <td>547.264099</td>\n",
       "      <td>482.142883</td>\n",
       "      <td>-1210.700195</td>\n",
       "      <td>-386.704407</td>\n",
       "      <td>484.923767</td>\n",
       "      <td>1400.397339</td>\n",
       "      <td>2142.133789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1960-07-31</td>\n",
       "      <td>566.634033</td>\n",
       "      <td>576.086548</td>\n",
       "      <td>588.730164</td>\n",
       "      <td>602.847534</td>\n",
       "      <td>613.312256</td>\n",
       "      <td>548.551086</td>\n",
       "      <td>-1049.558838</td>\n",
       "      <td>-299.192017</td>\n",
       "      <td>578.715820</td>\n",
       "      <td>1399.025879</td>\n",
       "      <td>2226.514404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1960-08-31</td>\n",
       "      <td>554.081116</td>\n",
       "      <td>568.410767</td>\n",
       "      <td>580.600281</td>\n",
       "      <td>594.198730</td>\n",
       "      <td>605.851440</td>\n",
       "      <td>542.382874</td>\n",
       "      <td>-1056.719116</td>\n",
       "      <td>-310.321533</td>\n",
       "      <td>543.106689</td>\n",
       "      <td>1420.388306</td>\n",
       "      <td>2138.160889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1960-09-30</td>\n",
       "      <td>503.825928</td>\n",
       "      <td>511.493469</td>\n",
       "      <td>520.782532</td>\n",
       "      <td>530.070435</td>\n",
       "      <td>551.331299</td>\n",
       "      <td>656.870056</td>\n",
       "      <td>-957.937927</td>\n",
       "      <td>-157.202362</td>\n",
       "      <td>644.464355</td>\n",
       "      <td>1539.134644</td>\n",
       "      <td>2261.052490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1960-10-31</td>\n",
       "      <td>438.602539</td>\n",
       "      <td>445.856720</td>\n",
       "      <td>454.243591</td>\n",
       "      <td>462.382782</td>\n",
       "      <td>487.070221</td>\n",
       "      <td>662.375427</td>\n",
       "      <td>-926.544312</td>\n",
       "      <td>-206.266907</td>\n",
       "      <td>650.127808</td>\n",
       "      <td>1537.292480</td>\n",
       "      <td>2253.246094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1960-11-30</td>\n",
       "      <td>395.615570</td>\n",
       "      <td>402.616699</td>\n",
       "      <td>417.529083</td>\n",
       "      <td>430.389435</td>\n",
       "      <td>452.758911</td>\n",
       "      <td>499.940247</td>\n",
       "      <td>-1233.157471</td>\n",
       "      <td>-397.680908</td>\n",
       "      <td>492.310120</td>\n",
       "      <td>1396.803711</td>\n",
       "      <td>2209.155273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1960-12-31</td>\n",
       "      <td>433.402496</td>\n",
       "      <td>439.153748</td>\n",
       "      <td>448.741119</td>\n",
       "      <td>456.206573</td>\n",
       "      <td>471.018433</td>\n",
       "      <td>458.918365</td>\n",
       "      <td>-1393.779053</td>\n",
       "      <td>-589.960815</td>\n",
       "      <td>468.123871</td>\n",
       "      <td>1448.744263</td>\n",
       "      <td>2284.202637</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ds  Huber-lo-80.0  Huber-lo-50.0  Huber-median  Huber-hi-50.0  \\\n",
       "0  1960-01-31     392.046448     397.029694    402.842377     412.451111   \n",
       "1  1960-02-29     389.217041     398.078979    411.310669     426.811432   \n",
       "2  1960-03-31     434.787323     446.318176    456.515533     468.646667   \n",
       "3  1960-04-30     435.589081     443.395844    451.102966     458.542328   \n",
       "4  1960-05-31     442.144714     448.320862    455.896271     466.212524   \n",
       "5  1960-06-30     505.597168     513.204590    522.992188     535.911987   \n",
       "6  1960-07-31     566.634033     576.086548    588.730164     602.847534   \n",
       "7  1960-08-31     554.081116     568.410767    580.600281     594.198730   \n",
       "8  1960-09-30     503.825928     511.493469    520.782532     530.070435   \n",
       "9  1960-10-31     438.602539     445.856720    454.243591     462.382782   \n",
       "10 1960-11-30     395.615570     402.616699    417.529083     430.389435   \n",
       "11 1960-12-31     433.402496     439.153748    448.741119     456.206573   \n",
       "\n",
       "    Huber-hi-80.0      Normal  Normal-lo-80.0  Normal-lo-50.0  Normal-median  \\\n",
       "0      420.441254  370.554321    -1246.564331     -466.951233     374.260681   \n",
       "1      462.116272  431.134827    -1016.146179     -376.702576     469.389313   \n",
       "2      486.479950  469.221069     -965.224670     -268.812164     455.846985   \n",
       "3      469.953857  544.345642    -1038.976440     -251.178711     535.880615   \n",
       "4      477.713348  400.593628    -1188.452881     -417.007935     426.566284   \n",
       "5      547.264099  482.142883    -1210.700195     -386.704407     484.923767   \n",
       "6      613.312256  548.551086    -1049.558838     -299.192017     578.715820   \n",
       "7      605.851440  542.382874    -1056.719116     -310.321533     543.106689   \n",
       "8      551.331299  656.870056     -957.937927     -157.202362     644.464355   \n",
       "9      487.070221  662.375427     -926.544312     -206.266907     650.127808   \n",
       "10     452.758911  499.940247    -1233.157471     -397.680908     492.310120   \n",
       "11     471.018433  458.918365    -1393.779053     -589.960815     468.123871   \n",
       "\n",
       "    Normal-hi-50.0  Normal-hi-80.0  \n",
       "0      1214.356934     1953.910645  \n",
       "1      1195.809082     1874.808838  \n",
       "2      1197.529175     1983.282349  \n",
       "3      1403.179932     2100.239990  \n",
       "4      1202.106201     2103.583008  \n",
       "5      1400.397339     2142.133789  \n",
       "6      1399.025879     2226.514404  \n",
       "7      1420.388306     2138.160889  \n",
       "8      1539.134644     2261.052490  \n",
       "9      1537.292480     2253.246094  \n",
       "10     1396.803711     2209.155273  \n",
       "11     1448.744263     2284.202637  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By default NeuralForecast produces forecast intervals\n",
    "# In this case the lo-x and high-x levels represent the \n",
    "# low and high bounds of the prediction accumulating x% probability\n",
    "Y_hat_df = Y_hat_df.reset_index(drop=True)\n",
    "Y_hat_df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Plot and Evaluate Predictions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we plot the forecasts of both models againts the real values.\n",
    "\n",
    "And evaluate the accuracy of the `NHITS-Huber` and `NHITS-Normal` forecasters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize = (20, 7))\n",
    "plot_df = pd.concat([Y_train_df, Y_hat_df]).set_index('ds') # Concatenate the train and forecast dataframes\n",
    "plot_df[['y', 'Huber-median', 'Normal-median']].plot(ax=ax, linewidth=2)\n",
    "\n",
    "ax.set_title('Noisy AirPassengers Forecast', fontsize=22)\n",
    "ax.set_ylabel('Monthly Passengers', fontsize=20)\n",
    "ax.set_xlabel('Timestamp [t]', fontsize=20)\n",
    "ax.legend(prop={'size': 15})\n",
    "ax.grid()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To evaluate the median predictions we use the mean average percentage error (MAPE), defined as follows:\n",
    "\n",
    "$$\\mathrm{MAPE}(\\mathbf{y}_{\\tau}, \\hat{\\mathbf{y}}_{\\tau}) = \\mathrm{mean}\\left(\\frac{|\\mathbf{y}_{\\tau}-\\hat{\\mathbf{y}}_{\\tau}|}{|\\mathbf{y}_{\\tau}|}\\right)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Huber MAPE: 4.2%\n",
      "Normal MAPE: 16.2%\n"
     ]
    }
   ],
   "source": [
    "#from neuralforecast.losses.numpy import mape, mqloss\n",
    "\n",
    "huber_mae = mape(y=Y_test_df['y'], y_hat=Y_hat_df['Huber-median'])\n",
    "normal_mae = mape(y=Y_test_df['y'], y_hat=Y_hat_df['Normal-median'])\n",
    "\n",
    "print(f'Huber MAPE: {huber_mae:.1%}')\n",
    "print(f'Normal MAPE: {normal_mae:.1%}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To evaluate the coherent probabilistic predictions we use the Continuous Ranked Probability Score (CRPS), defined as follows:\n",
    "\n",
    "$$\\mathrm{CRPS}(\\hat{F}_{\\tau},\\mathbf{y}_{\\tau}) = \\int^{1}_{0} \\mathrm{QL}(\\hat{F}_{\\tau}, y_{\\tau})_{q} dq$$\n",
    "\n",
    "As you can see robust regression improvements can reflect in the probabilistic forecasts too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Huber CRPS: 6.139\n",
      "Normal CRPS: 157.5\n"
     ]
    }
   ],
   "source": [
    "huber_qcols = ['Huber-lo-80.0', 'Huber-lo-50.0',\t'Huber-median',\t'Huber-hi-50.0', 'Huber-hi-80.0']\n",
    "normal_qcols = ['Normal-lo-80.0', 'Normal-lo-50.0',\t'Normal-median',\t'Normal-hi-50.0', 'Normal-hi-80.0']\n",
    "\n",
    "huber_crps = mqloss(y=Y_test_df['y'], y_hat=Y_hat_df[huber_qcols], \n",
    "                   quantiles=np.array(quantiles))\n",
    "normal_crps = mqloss(y=Y_test_df['y'], y_hat=Y_hat_df[normal_qcols], \n",
    "                    quantiles=np.array(quantiles))\n",
    "\n",
    "print(f'Huber CRPS: {huber_crps:.4}')\n",
    "print(f'Normal CRPS: {normal_crps:.4}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "- [Huber Peter, J (1964). \"Robust Estimation of a Location Parameter\". Annals of Statistics.](https://projecteuclid.org/journals/annals-of-mathematical-statistics/volume-35/issue-1/Robust-Estimation-of-a-Location-Parameter/10.1214/aoms/1177703732.full)<br>\n",
    "- [Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov (2014).\"Dropout: A Simple Way to Prevent Neural Networks from Overfitting\". Journal of Machine Learning Research.](https://jmlr.org/papers/v15/srivastava14a.html)<br>\n",
    "- [Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski (2023). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. Accepted at AAAI 2023.](https://arxiv.org/abs/2201.12886)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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