Forecasting time series with gradient boosting: Skforecast, XGBoost, LightGBM, Scikit-learn and CatBoost

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Introduction¶

Gradient boosting models have gained popularity in the machine learning community due to their ability to achieve excellent results in a wide range of use cases, including both regression and classification. Although these models have traditionally been less common in forecasting, they can be highly effective in this domain. Some of the key benefits of using gradient boosting models for forecasting include:

• The ease with which exogenous variables can be included in the model, in addition to autoregressive variables.

• The ability to capture non-linear relationships between variables.

• High scalability, allowing models to handle large volumes of data.

• Some implementations allow the inclusion of categorical variables without the need for additional encoding, such as one-hot encoding.

Despite these benefits, the use of machine learning models for forecasting can present several challenges that can make analysts reluctant to use them, the main ones being:

• Transforming the data so that it can be used as a regression problem.

• Depending on how many future predictions are needed (prediction horizon), an iterative process may be required where each new prediction is based on previous ones.

• Model validation requires specific strategies such as backtesting, walk-forward validation or time series cross-validation. Traditional cross-validation cannot be used.

The skforecast library provides automated solutions to these challenges, making it easier to apply and validate machine learning models to forecasting problems. The library supports several advanced gradient boosting models, including XGBoost, LightGBM, Catboost and scikit-learn HistGradientBoostingRegressor. This document shows how to use them to build accurate forecasting models. To ensure a smooth learning experience, an initial exploration of the data is performed. Then, the modeling process is explained step by step, starting with a recursive model utilizing a LightGBM regressor and moving on to a model incorporating exogenous variables and various coding strategies. The document concludes by demonstrating the use of other gradient boosting model implementations, including XGBoost, CatBoost, and the scikit-learn HistGradientBoostingRegressor.

Use case¶

Bicycle sharing is a popular shared transport service that provides bicycles to individuals for short-term use. These systems typically provide bike docks where riders can borrow a bike and return it to any dock belonging to the same system. The docks are equipped with special bike racks that secure the bike and only release it via computer control. One of the major challenges faced by operators of these systems is the need to redistribute bikes to ensure that there are bikes available at all docks, as well as free spaces for returns.

In order to improve the planning and execution of bicycle distribution, it is proposed to create a model capable of forecasting the number of users over the next 36 hours. In this way, at 12:00 every day, the company in charge of managing the system will be able to know the expected users for the rest of the day (12 hours) and the next day (24 hours).

For illustrative purposes, the current example only models a single station, but it can be easily extended to cover multiple stations unsing global multi-series forecasting, thereby improving the management of bike-sharing systems on a larger scale.

Libraries¶

Libraries used in this document.

# Data processing
# ==============================================================================
import numpy as np
import pandas as pd
from astral.sun import sun
from astral import LocationInfo
from skforecast.datasets import fetch_dataset
from feature_engine.datetime import DatetimeFeatures
from feature_engine.creation import CyclicalFeatures
from feature_engine.timeseries.forecasting import WindowFeatures

# Plots
# ==============================================================================
import matplotlib.pyplot as plt
from statsmodels.graphics.tsaplots import plot_acf
from statsmodels.graphics.tsaplots import plot_pacf
from skforecast.plot import plot_residuals, calculate_lag_autocorrelation
import plotly.graph_objects as go
import plotly.io as pio
import plotly.offline as poff
pio.templates.default = "seaborn"
poff.init_notebook_mode(connected=True)
plt.style.use('seaborn-v0_8-darkgrid')

# Modelling and Forecasting
# ==============================================================================
import xgboost
import lightgbm
import catboost
import sklearn
import shap
from xgboost import XGBRegressor
from lightgbm import LGBMRegressor
from catboost import CatBoostRegressor
from sklearn.ensemble import HistGradientBoostingRegressor
from sklearn.preprocessing import (
    OneHotEncoder,
    OrdinalEncoder,
    FunctionTransformer,
    PolynomialFeatures,
)
from sklearn.feature_selection import RFECV
from sklearn.pipeline import make_pipeline
from sklearn.compose import make_column_transformer, make_column_selector

import skforecast
from skforecast.recursive import ForecasterEquivalentDate, ForecasterRecursive
from skforecast.model_selection import (
    TimeSeriesFold,
    OneStepAheadFold,
    bayesian_search_forecaster,
    backtesting_forecaster,
)
from skforecast.preprocessing import RollingFeatures
from skforecast.feature_selection import select_features
from skforecast.metrics import calculate_coverage

# Warnings configuration
# ==============================================================================
import warnings
warnings.filterwarnings('once')

color = '\033[1m\033[38;5;208m' 
print(f"{color}Version skforecast: {skforecast.__version__}")
print(f"{color}Version scikit-learn: {sklearn.__version__}")
print(f"{color}Version lightgbm: {lightgbm.__version__}")
print(f"{color}Version xgboost: {xgboost.__version__}")
print(f"{color}Version catboost: {catboost.__version__}")
print(f"{color}Version pandas: {pd.__version__}")
print(f"{color}Version numpy: {np.__version__}")

Version skforecast: 0.15.0
Version scikit-learn: 1.5.2
Version lightgbm: 4.6.0
Version xgboost: 2.1.4
Version catboost: 1.2.7
Version pandas: 2.2.3
Version numpy: 1.26.4

Data¶

The data in this document represent the hourly usage of the bike share system in the city of Washington, D.C. during the years 2011 and 2012. In addition to the number of users per hour, information about weather conditions and holidays is available. The original data was obtained from the UCI Machine Learning Repository and has been previously cleaned (code) applying the following modifications:

• Renamed columns with more descriptive names.

• Renamed categories of the weather variables. The category of heavy_rain has been combined with that of rain.

• Denormalized temperature, humidity and wind variables.

• date_time variable created and set as index.

• Imputed missing values by forward filling.

# Downloading data
# ==============================================================================
data = fetch_dataset('bike_sharing', raw=True)

bike_sharing
------------
Hourly usage of the bike share system in the city of Washington D.C. during the
years 2011 and 2012. In addition to the number of users per hour, information
about weather conditions and holidays is available.
Fanaee-T,Hadi. (2013). Bike Sharing Dataset. UCI Machine Learning Repository.
https://doi.org/10.24432/C5W894.
Shape of the dataset: (17544, 12)

# Preprocessing data (setting index and frequency)
# ==============================================================================
data = data[['date_time', 'users', 'holiday', 'weather', 'temp', 'atemp', 'hum', 'windspeed']]
data['date_time'] = pd.to_datetime(data['date_time'], format='%Y-%m-%d %H:%M:%S')
data = data.set_index('date_time')
if pd.__version__ < '2.2':
    data = data.asfreq('H')
else:
    data = data.asfreq('h')
data = data.sort_index()
data.head()

To facilitate the training of the models, the search for optimal hyperparameters and the evaluation of their predictive accuracy, the data are divided into three separate sets: training, validation and test.

# Split train-validation-test
# ==============================================================================
end_train = '2012-04-30 23:59:00'
end_validation = '2012-08-31 23:59:00'
data_train = data.loc[: end_train, :]
data_val   = data.loc[end_train:end_validation, :]
data_test  = data.loc[end_validation:, :]

print(f"Dates train      : {data_train.index.min()} --- {data_train.index.max()}  (n={len(data_train)})")
print(f"Dates validacion : {data_val.index.min()} --- {data_val.index.max()}  (n={len(data_val)})")
print(f"Dates test       : {data_test.index.min()} --- {data_test.index.max()}  (n={len(data_test)})")

Dates train      : 2011-01-01 00:00:00 --- 2012-04-30 23:00:00  (n=11664)
Dates validacion : 2012-05-01 00:00:00 --- 2012-08-31 23:00:00  (n=2952)
Dates test       : 2012-09-01 00:00:00 --- 2012-12-31 23:00:00  (n=2928)

Data exploration¶

Graphical exploration of time series can be an effective way of identifying trends, patterns, and seasonal variations. This, in turn, helps to guide the selection of the most appropriate forecasting model.

Plot time series¶

Full time series

# Interactive plot of time series
# ==============================================================================
fig = go.Figure()
fig.add_trace(go.Scatter(x=data_train.index, y=data_train['users'], mode='lines', name='Train'))
fig.add_trace(go.Scatter(x=data_val.index, y=data_val['users'], mode='lines', name='Validation'))
fig.add_trace(go.Scatter(x=data_test.index, y=data_test['users'], mode='lines', name='Test'))
fig.update_layout(
    title  = 'Number of users',
    xaxis_title="Time",
    yaxis_title="Users",
    legend_title="Partition:",
    width=800,
    height=400,
    margin=dict(l=20, r=20, t=35, b=20),
    legend=dict(orientation="h", yanchor="top", y=1, xanchor="left", x=0.001)
)
#fig.update_xaxes(rangeslider_visible=True)
fig.show()