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Feature importances
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Introduction to forecasting
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Skforecast Docs
GitHub
Home
Home
Welcome to skforecast
Introduction to forecasting
Quick start
User Guides
Examples and tutorials
API Reference
FAQ and Tips
Releases
Introduction to forecasting
Introduction to forecasting
Introduction to forecasting
Forecaster Parameters
Forecaster Attributes
Quick start
User Guides
User Guides
Input data
Recursive multi-step forecasting
Direct multi-step forecasting
Independent multi-time series forecasting
Dependent multivariate series forecasting
Exogenous variables
Custom predictors
Weighted time series forecasting
Backtesting forecaster
Hyperparameter tuning and lags selection
Scikit-learn Transformers and Pipelines
Probabilistic forecasting
Feature importances
Feature importances
Table of contents
Libraries
Data
Extract feature importances from trained forecaster
Categorical features
Forecasting with XGBoost and LightGBM
Forecasting SARIMAX and ARIMA models
Save and load forecaster
Forecaster in production
Shap-values
Skforecast in GPU
Plot forecaster residuals
Examples and tutorials
Examples and tutorials
Examples and tutorials
API Reference
API Reference
ForecasterAutoreg
ForecasterAutoregCustom
ForecasterAutoregDirect
ForecasterMultiSeries
ForecasterMultiSeriesCustom
ForecasterMultiVariate
ForecasterSarimax
model_selection
model_selection_multiseries
model_selection_sarimax
plot
utils
exceptions
FAQ and Tips
FAQ and Tips
Table of contents
Avoid negative predictions when forecasting
Forecasting time series with missing values
Cyclical features in time series
Profiling skforecast
Releases
Table of contents
Libraries
Data
Extract feature importances from trained forecaster