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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: shap in c:\\users\\ospra\\miniconda3\\envs\\nixtla\\lib\\site-packages (0.46.0)\n",
"Requirement already satisfied: numpy in c:\\users\\ospra\\miniconda3\\envs\\nixtla\\lib\\site-packages (from shap) (1.26.4)\n",
"Requirement already satisfied: scipy in c:\\users\\ospra\\miniconda3\\envs\\nixtla\\lib\\site-packages (from shap) (1.14.1)\n",
"Requirement already satisfied: scikit-learn in c:\\users\\ospra\\miniconda3\\envs\\nixtla\\lib\\site-packages (from shap) (1.5.2)\n",
"Requirement already satisfied: pandas in c:\\users\\ospra\\miniconda3\\envs\\nixtla\\lib\\site-packages (from shap) (2.2.3)\n",
"Requirement already satisfied: tqdm>=4.27.0 in c:\\users\\ospra\\miniconda3\\envs\\nixtla\\lib\\site-packages (from shap) (4.67.1)\n",
"Requirement already satisfied: packaging>20.9 in c:\\users\\ospra\\miniconda3\\envs\\nixtla\\lib\\site-packages (from shap) (24.2)\n",
"Requirement already satisfied: slicer==0.0.8 in c:\\users\\ospra\\miniconda3\\envs\\nixtla\\lib\\site-packages (from shap) (0.0.8)\n",
"Requirement already satisfied: numba in c:\\users\\ospra\\miniconda3\\envs\\nixtla\\lib\\site-packages (from shap) (0.60.0)\n",
"Requirement already satisfied: cloudpickle in c:\\users\\ospra\\miniconda3\\envs\\nixtla\\lib\\site-packages (from shap) (3.1.0)\n",
"Requirement already satisfied: colorama in c:\\users\\ospra\\miniconda3\\envs\\nixtla\\lib\\site-packages (from tqdm>=4.27.0->shap) (0.4.6)\n",
"Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in c:\\users\\ospra\\miniconda3\\envs\\nixtla\\lib\\site-packages (from numba->shap) (0.43.0)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\ospra\\miniconda3\\envs\\nixtla\\lib\\site-packages (from pandas->shap) (2.9.0.post0)\n",
"Requirement already satisfied: pytz>=2020.1 in c:\\users\\ospra\\miniconda3\\envs\\nixtla\\lib\\site-packages (from pandas->shap) (2024.2)\n",
"Requirement already satisfied: tzdata>=2022.7 in c:\\users\\ospra\\miniconda3\\envs\\nixtla\\lib\\site-packages (from pandas->shap) (2024.2)\n",
"Requirement already satisfied: joblib>=1.2.0 in c:\\users\\ospra\\miniconda3\\envs\\nixtla\\lib\\site-packages (from scikit-learn->shap) (1.4.2)\n",
"Requirement already satisfied: threadpoolctl>=3.1.0 in c:\\users\\ospra\\miniconda3\\envs\\nixtla\\lib\\site-packages (from scikit-learn->shap) (3.5.0)\n",
"Requirement already satisfied: six>=1.5 in c:\\users\\ospra\\miniconda3\\envs\\nixtla\\lib\\site-packages (from python-dateutil>=2.8.2->pandas->shap) (1.16.0)\n"
]
}
],
"source": [
"#| hide\n",
"!pip install -Uqq nixtla\n",
"!pip install shap"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"from nixtla.utils import in_colab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"IN_COLAB = in_colab()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" from nixtla.utils import colab_badge\n",
" from dotenv import load_dotenv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SHAP Values for TimeGPT and TimeGEN"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"SHAP (SHapley Additive exPlanation) values use game theory to explain the output of any machine learning model. It allows us to explore in detail how exogenous features impact the final forecast, both at a single forecast step or over the entire horizon. \n",
"\n",
"When you forecast with exogenous features, you can access the SHAP values for all series at each prediction step, and use the popular [shap](https://shap.readthedocs.io/en/latest/) Python package to make different plots and explain the impact of the features.\n",
"\n",
"This tutorial assumes knowledge on forecasting with exogenous features, so make sure to read our tutorial on [exogenous variables](https://docs.nixtla.io/docs/tutorials-exogenous_variables). Also, the `shap` package must be installed separately as it is not a dependency of `nixtla`.\n",
"\n",
"`shap` can be installed from either [PyPI](https://pypi.org/project/shap/) or [conda-forge](https://anaconda.org/conda-forge/shap):\n",
"```\n",
"pip install shap\n",
"\n",
"or\n",
"\n",
"conda install -c conda-forge shap\n",
"```\n",
"For the official documentation of SHAP, visit: https://shap.readthedocs.io/en/latest/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/tutorials/21_shap_values.ipynb)"
],
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"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#| echo: false\n",
"if not IN_COLAB:\n",
" load_dotenv()\n",
" colab_badge('docs/tutorials/21_shap_values')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Import packages\n",
"First, we import the required packages and initialize the Nixtla client."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from nixtla import NixtlaClient"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nixtla_client = NixtlaClient(\n",
" # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n",
" api_key = 'my_api_key_provided_by_nixtla'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 👍 Use an Azure AI endpoint\n",
"> \n",
"> To use an Azure AI endpoint, remember to set also the `base_url` argument:\n",
"> \n",
"> `nixtla_client = NixtlaClient(base_url=\"you azure ai endpoint\", api_key=\"your api_key\")`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" nixtla_client = NixtlaClient()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Load data\n",
"\n",
"In this example on SHAP values, we will use exogenous variables (also known as covariates) to improve the accuracy of electricity market forecasts. We'll work with a well-known dataset called `EPF`, which is publicly accessible [here](https://zenodo.org/records/4624805). \n",
"\n",
"This dataset includes data from five different electricity markets, each with unique price dynamics, such as varying frequencies and occurrences of negative prices, zeros, and price spikes. Since electricity prices are influenced by exogenous factors, each dataset also contains two additional time series: day-ahead forecasts of two significant exogenous factors specific to each market.\n",
"\n",
"For simplicity, we will focus on the Belgian electricity market (BE). This dataset includes hourly prices (`y`), day-ahead forecasts of load (`Exogenous1`), and electricity generation (`Exogenous2`). It also includes one-hot encoding to indicate whether a specific date is a specific day of the week. Eg.: Monday (`day_0 = 1`), a Tuesday (`day_1 = 1`), and so on.\n",
"\n",
"If your data depends on exogenous factors or covariates such as prices, discounts, special holidays, weather, etc., you can follow a similar structure."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
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" <th>0</th>\n",
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" <th>1</th>\n",
" <td>BE</td>\n",
" <td>2016-10-22 01:00:00</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>BE</td>\n",
" <td>2016-10-22 02:00:00</td>\n",
" <td>37.10</td>\n",
" <td>51896.0</td>\n",
" <td>44927.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>BE</td>\n",
" <td>2016-10-22 03:00:00</td>\n",
" <td>44.75</td>\n",
" <td>48428.0</td>\n",
" <td>44483.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>BE</td>\n",
" <td>2016-10-22 04:00:00</td>\n",
" <td>37.10</td>\n",
" <td>46721.0</td>\n",
" <td>44338.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" unique_id ds y Exogenous1 Exogenous2 day_0 day_1 \\\n",
"0 BE 2016-10-22 00:00:00 70.00 57253.0 49593.0 0.0 0.0 \n",
"1 BE 2016-10-22 01:00:00 37.10 51887.0 46073.0 0.0 0.0 \n",
"2 BE 2016-10-22 02:00:00 37.10 51896.0 44927.0 0.0 0.0 \n",
"3 BE 2016-10-22 03:00:00 44.75 48428.0 44483.0 0.0 0.0 \n",
"4 BE 2016-10-22 04:00:00 37.10 46721.0 44338.0 0.0 0.0 \n",
"\n",
" day_2 day_3 day_4 day_5 day_6 \n",
"0 0.0 0.0 0.0 1.0 0.0 \n",
"1 0.0 0.0 0.0 1.0 0.0 \n",
"2 0.0 0.0 0.0 1.0 0.0 \n",
"3 0.0 0.0 0.0 1.0 0.0 \n",
"4 0.0 0.0 0.0 1.0 0.0 "
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"market = \"BE\"\n",
"df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short-with-ex-vars.csv')\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Forecasting electricity prices using exogenous variables\n",
"\n",
"To produce forecasts we also have to add the future values of the exogenous variables. \n",
"\n",
"If your forecast depends on other variables, it is important to ensure that those variables are available at the time of forecasting. In this example, we know that the price of electricity depends on the demand (`Exogenous1`) and the quantity produced (`Exogenous2`). Thus, we need to have those future values available at the time of forecasting. If those values were not available, we can always [use TimeGPT to forecast them](https://docs.nixtla.io/docs/tutorials-exogenous_variables).\n",
"\n",
"Here, we read a dataset that contains the future values of our features. In this case, we want to predict 24 steps ahead, therefore each `unique_id` will have 24 observations.\n",
"\n",
"::: {.callout-important}\n",
"If you want to use exogenous variables when forecasting with TimeGPT, you need to have the future values of those exogenous variables too.\n",
"::: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
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" <th>0</th>\n",
" <td>BE</td>\n",
" <td>2016-12-31 00:00:00</td>\n",
" <td>70318.0</td>\n",
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" <th>1</th>\n",
" <td>BE</td>\n",
" <td>2016-12-31 01:00:00</td>\n",
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" <th>2</th>\n",
" <td>BE</td>\n",
" <td>2016-12-31 02:00:00</td>\n",
" <td>68379.0</td>\n",
" <td>61571.0</td>\n",
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" <th>3</th>\n",
" <td>BE</td>\n",
" <td>2016-12-31 03:00:00</td>\n",
" <td>64972.0</td>\n",
" <td>60381.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>BE</td>\n",
" <td>2016-12-31 04:00:00</td>\n",
" <td>62900.0</td>\n",
" <td>60298.0</td>\n",
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" <td>0.0</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" unique_id ds Exogenous1 Exogenous2 day_0 day_1 day_2 \\\n",
"0 BE 2016-12-31 00:00:00 70318.0 64108.0 0.0 0.0 0.0 \n",
"1 BE 2016-12-31 01:00:00 67898.0 62492.0 0.0 0.0 0.0 \n",
"2 BE 2016-12-31 02:00:00 68379.0 61571.0 0.0 0.0 0.0 \n",
"3 BE 2016-12-31 03:00:00 64972.0 60381.0 0.0 0.0 0.0 \n",
"4 BE 2016-12-31 04:00:00 62900.0 60298.0 0.0 0.0 0.0 \n",
"\n",
" day_3 day_4 day_5 day_6 \n",
"0 0.0 0.0 1.0 0.0 \n",
"1 0.0 0.0 1.0 0.0 \n",
"2 0.0 0.0 1.0 0.0 \n",
"3 0.0 0.0 1.0 0.0 \n",
"4 0.0 0.0 1.0 0.0 "
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"future_ex_vars_df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short-future-ex-vars.csv')\n",
"future_ex_vars_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's call the `forecast` method, adding this information. To access the SHAP values, we also need to specify `feature_contributions=True` in the `forecast` method."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:nixtla.nixtla_client:Validating inputs...\n",
"INFO:nixtla.nixtla_client:Inferred freq: h\n",
"INFO:nixtla.nixtla_client:Querying model metadata...\n",
"INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
"INFO:nixtla.nixtla_client:Using future exogenous features: ['Exogenous1', 'Exogenous2', 'day_0', 'day_1', 'day_2', 'day_3', 'day_4', 'day_5', 'day_6']\n",
"INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n"
]
},
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" <td>51.632830</td>\n",
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" <td>66.088295</td>\n",
" <td>41.666843</td>\n",
" <td>37.177372</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>BE</td>\n",
" <td>2016-12-31 01:00:00</td>\n",
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" <td>60.176445</td>\n",
" <td>36.889767</td>\n",
" <td>31.325312</td>\n",
" </tr>\n",
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" <td>BE</td>\n",
" <td>2016-12-31 02:00:00</td>\n",
" <td>39.650543</td>\n",
" <td>46.256210</td>\n",
" <td>52.842808</td>\n",
" <td>33.044876</td>\n",
" <td>26.458277</td>\n",
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" <th>3</th>\n",
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" <td>2016-12-31 03:00:00</td>\n",
" <td>34.000072</td>\n",
" <td>44.015310</td>\n",
" <td>47.429000</td>\n",
" <td>23.984835</td>\n",
" <td>20.571144</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>BE</td>\n",
" <td>2016-12-31 04:00:00</td>\n",
" <td>33.785370</td>\n",
" <td>43.140503</td>\n",
" <td>48.581240</td>\n",
" <td>24.430239</td>\n",
" <td>18.989498</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" unique_id ds TimeGPT TimeGPT-hi-80 TimeGPT-hi-90 \\\n",
"0 BE 2016-12-31 00:00:00 51.632830 61.598820 66.088295 \n",
"1 BE 2016-12-31 01:00:00 45.750877 54.611988 60.176445 \n",
"2 BE 2016-12-31 02:00:00 39.650543 46.256210 52.842808 \n",
"3 BE 2016-12-31 03:00:00 34.000072 44.015310 47.429000 \n",
"4 BE 2016-12-31 04:00:00 33.785370 43.140503 48.581240 \n",
"\n",
" TimeGPT-lo-80 TimeGPT-lo-90 \n",
"0 41.666843 37.177372 \n",
"1 36.889767 31.325312 \n",
"2 33.044876 26.458277 \n",
"3 23.984835 20.571144 \n",
"4 24.430239 18.989498 "
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"timegpt_fcst_ex_vars_df = nixtla_client.forecast(df=df, \n",
" X_df=future_ex_vars_df, \n",
" h=24, \n",
" level=[80, 90],\n",
" feature_contributions=True)\n",
"timegpt_fcst_ex_vars_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Extract SHAP values\n",
"\n",
"Now that we have made predictions using exogenous features, we can then extract the SHAP values to understand their relevance using the `feature_contributions` attribute of the client. This returns a DataFrame containing the SHAP values and base values for each series, at each step in the horizon."
]
},
{
"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>unique_id</th>\n",
" <th>ds</th>\n",
" <th>TimeGPT</th>\n",
" <th>Exogenous1</th>\n",
" <th>Exogenous2</th>\n",
" <th>day_0</th>\n",
" <th>day_1</th>\n",
" <th>day_2</th>\n",
" <th>day_3</th>\n",
" <th>day_4</th>\n",
" <th>day_5</th>\n",
" <th>day_6</th>\n",
" <th>base_value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>BE</td>\n",
" <td>2016-12-31 00:00:00</td>\n",
" <td>51.632830</td>\n",
" <td>27.929638</td>\n",
" <td>-16.363607</td>\n",
" <td>0.081917</td>\n",
" <td>-1.883555</td>\n",
" <td>0.346484</td>\n",
" <td>-0.228611</td>\n",
" <td>0.424167</td>\n",
" <td>-3.411662</td>\n",
" <td>1.113910</td>\n",
" <td>43.624146</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>BE</td>\n",
" <td>2016-12-31 01:00:00</td>\n",
" <td>45.750877</td>\n",
" <td>17.678530</td>\n",
" <td>-12.240089</td>\n",
" <td>-0.758545</td>\n",
" <td>-0.077536</td>\n",
" <td>-0.160390</td>\n",
" <td>-0.309567</td>\n",
" <td>0.871469</td>\n",
" <td>-3.927268</td>\n",
" <td>1.218714</td>\n",
" <td>43.455560</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>BE</td>\n",
" <td>2016-12-31 02:00:00</td>\n",
" <td>39.650543</td>\n",
" <td>21.632694</td>\n",
" <td>-21.400244</td>\n",
" <td>-0.926842</td>\n",
" <td>-0.470276</td>\n",
" <td>-0.022417</td>\n",
" <td>-0.225389</td>\n",
" <td>0.220258</td>\n",
" <td>-3.927268</td>\n",
" <td>1.145736</td>\n",
" <td>43.624290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>BE</td>\n",
" <td>2016-12-31 03:00:00</td>\n",
" <td>34.000072</td>\n",
" <td>13.879354</td>\n",
" <td>-20.681124</td>\n",
" <td>-0.114050</td>\n",
" <td>-0.488141</td>\n",
" <td>0.048164</td>\n",
" <td>-0.126627</td>\n",
" <td>0.200692</td>\n",
" <td>-3.400485</td>\n",
" <td>1.144959</td>\n",
" <td>43.537330</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>BE</td>\n",
" <td>2016-12-31 04:00:00</td>\n",
" <td>33.785370</td>\n",
" <td>13.465129</td>\n",
" <td>-20.619830</td>\n",
" <td>-0.036112</td>\n",
" <td>-0.470496</td>\n",
" <td>0.048375</td>\n",
" <td>-0.126627</td>\n",
" <td>0.200692</td>\n",
" <td>-3.400485</td>\n",
" <td>1.144959</td>\n",
" <td>43.579760</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" unique_id ds TimeGPT Exogenous1 Exogenous2 day_0 \\\n",
"0 BE 2016-12-31 00:00:00 51.632830 27.929638 -16.363607 0.081917 \n",
"1 BE 2016-12-31 01:00:00 45.750877 17.678530 -12.240089 -0.758545 \n",
"2 BE 2016-12-31 02:00:00 39.650543 21.632694 -21.400244 -0.926842 \n",
"3 BE 2016-12-31 03:00:00 34.000072 13.879354 -20.681124 -0.114050 \n",
"4 BE 2016-12-31 04:00:00 33.785370 13.465129 -20.619830 -0.036112 \n",
"\n",
" day_1 day_2 day_3 day_4 day_5 day_6 base_value \n",
"0 -1.883555 0.346484 -0.228611 0.424167 -3.411662 1.113910 43.624146 \n",
"1 -0.077536 -0.160390 -0.309567 0.871469 -3.927268 1.218714 43.455560 \n",
"2 -0.470276 -0.022417 -0.225389 0.220258 -3.927268 1.145736 43.624290 \n",
"3 -0.488141 0.048164 -0.126627 0.200692 -3.400485 1.144959 43.537330 \n",
"4 -0.470496 0.048375 -0.126627 0.200692 -3.400485 1.144959 43.579760 "
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"shap_df = nixtla_client.feature_contributions\n",
"shap_df = shap_df.query(\"unique_id == @market\")\n",
"shap_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the Dataframe above, we can see that we have the SHAP values at every forecasting step, as well as the prediction from TimeGPT and the base value. Note that the base value is the prediction of the model if exogenous features were unknown. \n",
"\n",
"Therefore, the forecast from TimeGPT is equal to the sum of the base value and the SHAP values of each exogenous feature in a given row."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Make plots using `shap`\n",
"\n",
"Now that we have access to SHAP values we can use the `shap` package to make any plots that we want.\n",
"\n",
"### 5.1 Bar plot\n",
"Here, let's make bar plots for each series and their features, so we can see which features impacts the predictions the most."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import shap\n",
"import matplotlib.pyplot as plt\n",
"\n",
"shap_columns = shap_df.columns.difference(['unique_id', 'ds', 'TimeGPT', 'base_value'])\n",
"shap_values = shap_df[shap_columns].values # SHAP values matrix\n",
"base_values = shap_df['base_value'].values # Extract base values\n",
"features = shap_columns # Feature names\n",
"\n",
"# Create a SHAP values object\n",
"shap_obj = shap.Explanation(values=shap_values, base_values=base_values, feature_names=features)\n",
"\n",
"# Plot the bar plot for SHAP values\n",
"shap.plots.bar(shap_obj, max_display=len(features), show=False)\n",
"plt.title(f'SHAP values for {market}')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The plot above shows the average SHAP values for each feature across the entire horizon.\n",
"\n",
"Here, we see that `Exogenous1` is the most important feature, as it has the largest average contribution. Remember that it designates the expected energy demand, so we can see that this variable has a large impact on the final prediction. On the other hand, `day_2` is the least important feature, since it has the lowest value."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.2 Waterfall plot\n",
"\n",
"Now, let's see how we can make a waterfall plot to explore the the impact of features at a single prediction step. The code below selects a specific date. Of course, this can be modified for any series or date."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x600 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#| eval: false\n",
"selected_ds = shap_df['ds'].min()\n",
"\n",
"filtered_df = shap_df[shap_df['ds'] == selected_ds]\n",
"\n",
"shap_values = filtered_df[shap_columns].values.flatten()\n",
"base_value = filtered_df['base_value'].values[0]\n",
"features = shap_columns\n",
"\n",
"shap_obj = shap.Explanation(values=shap_values, base_values=base_value, feature_names=features)\n",
"\n",
"shap.plots.waterfall(shap_obj, show=False)\n",
"plt.title(f'Waterfall Plot: {market}, date: {selected_ds}')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the waterfall plot above, we can explore in more detail a single prediction. Here, we study the final prediction for the start of December 31th, 2016.\n",
"\n",
"The x-axis represents the value of our series. At the bottom, we see `E[f(X)]` which represents the baseline value (the predicted value if exogenous features were unknown). \n",
"\n",
"Then, we see how each feature has impacted the final forecast. Features like `day_3`, `day_1`, `day_5`, `Exogenous2` all push the forecast to the left (smaller value). On the other hand, `day_0`, `day_2`, `day_4`, `day_6` and `Exogenous1` push it to the right (larger value). \n",
"\n",
"Let’s think about this for a moment. In the introduction, we stated that `Exogenous1` represents electricity load, whereas `Exogenous2` represents electricity generation. \n",
"* `Exogenous1`, the electricity load, adds positively to the overall prediction. This seems reasonable: if we expect a higher demand, we might expect the price to go up.\n",
"* `Exogenous2`, on the other hand, adds negatively to the overall prediction. This seems reasonable too: if there’s a higher electricity generation, we expect the price to be lower. Hence, a negative contribution to the forecast for `Exogenous2`.\n",
"\n",
"At the top right, we see f(x) which is the final output of the model after considering the impact of the exogenous features. Notice that this value corresponds to the final prediction from TimeGPT. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.3 Heatmap\n",
"We can also do a heatmap plot to see how each feature impacts the final prediction. Here, we only need to select a specific series."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shap_columns = shap_df.columns.difference(['unique_id', 'ds', 'TimeGPT', 'base_value'])\n",
"shap_values = shap_df[shap_columns].values \n",
"feature_names = shap_columns.tolist()\n",
"\n",
"shap_obj = shap.Explanation(values=shap_values, feature_names=feature_names)\n",
"\n",
"shap.plots.heatmap(shap_obj, show=False)\n",
"plt.title(f'SHAP Heatmap (Unique ID: NP)')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the heatmap, we basically see a breakdown of each each feature impacts the final predciton at each timestep.\n",
"\n",
"On the x-axis, we have the number of instances, which corresponds to the number of prediction steps (24 in this case, since our horizon is set to 24h). On the y-axis, we have the name of the exogenous features.\n",
"\n",
"First, notice that the ordering is the same as in the bar plot, where `Exogenous1` is the most important, and `day_6` is the least important.\n",
"\n",
"Then, the color of the heatmap indiciates if the feature tends to increase of decrease the final prediction at each forecasting step. For example, `Exogenous1` always increases predictions across all 24 hours in the forecast horizon.\n",
"\n",
"We also see that all days except `day_5` do not have a very large impact at any forecasting step, indicating that they barely impacting the final prediction."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ultimately, the `feature_contributions` attribute gives you access to all the necessary information to explain the impact of exogenous features using the `shap` package."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "python3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
nbs/docs/tutorials/22_how_to_improve_forecast_accuracy.ipynb 0 → 100644
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"!pip install -Uqq nixtla"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"from nixtla.utils import in_colab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide \n",
"IN_COLAB = in_colab()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" from nixtla.utils import colab_badge\n",
" from dotenv import load_dotenv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Controlling the level of fine-tuning\n",
"It is possible to control the depth of fine-tuning with the `finetune_depth` parameter.\n",
"\n",
"`finetune_depth` takes values among `[1, 2, 3, 4, 5]`. By default, it is set to 1, which means that a small set of the model's parameters are being adjusted, whereas a value of 5 fine-tunes the maximum amount of parameters. \n",
"\n",
"Increasing `finetune_depth` also increases the time to generate predictions. While it can generate better results, we must be careful to not overfit the model, in which case the predictions may not be as accurate.\n",
"\n",
"Let's run a small experiment to see how `finetune_depth` impacts the performance."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/tutorials/23_finetune_depth_finetuning.ipynb)"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#| echo: false\n",
"if not IN_COLAB:\n",
" load_dotenv() \n",
" colab_badge('docs/tutorials/23_finetune_depth_finetuning')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Import packages\n",
"First, we import the required packages and initialize the Nixtla client"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from nixtla import NixtlaClient\n",
"from utilsforecast.losses import mae, mse\n",
"from utilsforecast.evaluation import evaluate"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nixtla_client = NixtlaClient(\n",
" # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n",
" api_key = 'my_api_key_provided_by_nixtla'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 👍 Use an Azure AI endpoint\n",
"> \n",
"> To use an Azure AI endpoint, remember to set also the `base_url` argument:\n",
"> \n",
"> `nixtla_client = NixtlaClient(base_url=\"you azure ai endpoint\", api_key=\"your api_key\")`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"if not IN_COLAB:\n",
" nixtla_client = NixtlaClient()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Load data"
]
},
{
"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>timestamp</th>\n",
" <th>value</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",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" timestamp value\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"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv')\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we split the data into a training and test set so that we can measure the performance of the model as we vary `finetune_depth`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train = df[:-24]\n",
"test = df[-24:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we fine-tune TimeGPT and vary `finetune_depth` to measure the impact on performance."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Fine-tuning with `finetune_depth`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 📘 Available models in Azure AI\n",
">\n",
"> If you are using an Azure AI endpoint, please be sure to set `model=\"azureai\"`:\n",
">\n",
"> `nixtla_client.forecast(..., model=\"azureai\")`\n",
"> \n",
"> For the public API, we support two models: `timegpt-1` and `timegpt-1-long-horizon`. \n",
"> \n",
"> By default, `timegpt-1` is used. Please see [this tutorial](https://docs.nixtla.io/docs/tutorials-long_horizon_forecasting) on how and when to use `timegpt-1-long-horizon`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As mentioned above, `finetune_depth` controls how many parameters from TimeGPT are fine-tuned on your particular dataset. If the value is set to 1, only a few parameters are fine-tuned. Setting it to 5 means that all parameters of the model will be fine-tuned. \n",
"\n",
"Using a large value for `finetune_depth` can lead to better performances for large datasets with complex patterns. However, it can also lead to overfitting, in which case the accuracy of the forecasts may degrade, as we will see from the small experiment below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:nixtla.nixtla_client:Validating inputs...\n",
"INFO:nixtla.nixtla_client:Inferred freq: MS\n",
"INFO:nixtla.nixtla_client:Querying model metadata...\n",
"WARNING:nixtla.nixtla_client:The specified horizon \"h\" exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.\n",
"INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
"INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n",
"INFO:nixtla.nixtla_client:Validating inputs...\n",
"INFO:nixtla.nixtla_client:Inferred freq: MS\n",
"WARNING:nixtla.nixtla_client:The specified horizon \"h\" exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.\n",
"INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
"INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n",
"INFO:nixtla.nixtla_client:Validating inputs...\n",
"INFO:nixtla.nixtla_client:Inferred freq: MS\n",
"WARNING:nixtla.nixtla_client:The specified horizon \"h\" exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.\n",
"INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
"INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n",
"INFO:nixtla.nixtla_client:Validating inputs...\n",
"INFO:nixtla.nixtla_client:Inferred freq: MS\n",
"WARNING:nixtla.nixtla_client:The specified horizon \"h\" exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.\n",
"INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
"INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n",
"INFO:nixtla.nixtla_client:Validating inputs...\n",
"INFO:nixtla.nixtla_client:Inferred freq: MS\n",
"WARNING:nixtla.nixtla_client:The specified horizon \"h\" exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.\n",
"INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
"INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n"
]
}
],
"source": [
"depths = [1, 2, 3, 4, 5]\n",
"\n",
"test = test.copy()\n",
"\n",
"for depth in depths:\n",
" preds_df = nixtla_client.forecast(\n",
" df=train, \n",
" h=24, \n",
" finetune_steps=5,\n",
" finetune_depth=depth,\n",
" time_col='timestamp', \n",
" target_col='value')\n",
"\n",
" preds = preds_df['TimeGPT'].values\n",
"\n",
" test.loc[:,f'TimeGPT_depth{depth}'] = preds"
]
},
{
"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>unique_id</th>\n",
" <th>metric</th>\n",
" <th>TimeGPT_depth1</th>\n",
" <th>TimeGPT_depth2</th>\n",
" <th>TimeGPT_depth3</th>\n",
" <th>TimeGPT_depth4</th>\n",
" <th>TimeGPT_depth5</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>mae</td>\n",
" <td>22.675540</td>\n",
" <td>17.908963</td>\n",
" <td>21.318518</td>\n",
" <td>24.745096</td>\n",
" <td>28.734302</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>mse</td>\n",
" <td>677.254283</td>\n",
" <td>461.320852</td>\n",
" <td>676.202126</td>\n",
" <td>991.835359</td>\n",
" <td>1119.722602</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" unique_id metric TimeGPT_depth1 TimeGPT_depth2 TimeGPT_depth3 \\\n",
"0 0 mae 22.675540 17.908963 21.318518 \n",
"1 0 mse 677.254283 461.320852 676.202126 \n",
"\n",
" TimeGPT_depth4 TimeGPT_depth5 \n",
"0 24.745096 28.734302 \n",
"1 991.835359 1119.722602 "
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test['unique_id'] = 0\n",
"\n",
"evaluation = evaluate(test, metrics=[mae, mse], time_col=\"timestamp\", target_col=\"value\")\n",
"evaluation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the result above, we can see that a `finetune_depth` of 2 achieves the best results since it has the lowest MAE and MSE. \n",
"\n",
"Also notice that with a `finetune_depth` of 4 and 5, the performance degrades, which is a clear sign of overfitting. \n",
"\n",
"Thus, keep in mind that fine-tuning can be a bit of trial and error. You might need to adjust the number of `finetune_steps` and the level of `finetune_depth` based on your specific needs and the complexity of your data. Usually, a higher `finetune_depth` works better for large datasets. In this specific tutorial, since we were forecasting a single series with a very short dataset, increasing the depth led to overfitting.\n",
"\n",
"It's recommended to monitor the model's performance during fine-tuning and adjust as needed. Be aware that more `finetune_steps` and a larger value of `finetune_depth` may lead to longer training times and could potentially lead to overfitting if not managed properly. "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "python3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 2
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nbs/docs/use-cases/1_forecasting_web_traffic.ipynb 0 → 100644
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nbs/docs/use-cases/2_bitcoin_price_prediction.ipynb 0 → 100644
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