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Introduction to forecasting
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Skforecast Docs
GitHub
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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
Quick start
Quick start
Quick start
Forecaster Parameters
Forecaster Attributes
User Guides
User Guides
Input data
Input data
Table of contents
Libraries
Data
Train and predict using input with datetime and frequency index
Train and predict using input without datetime index
Recursive multi-step forecasting
Direct multi-step forecasting
Independent multi-time series forecasting
Dependent multivariate series forecasting
ARIMA and SARIMAX forecasting
Foreasting baseline
Exogenous variables
Custom predictors
Weighted time series forecasting
Backtesting forecaster
Hyperparameter tuning and lags selection
Scikit-learn Transformers and Pipelines
Probabilistic forecasting
Categorical features
Calendars features
Forecasting with XGBoost and LightGBM
Forecaster in production
Save and load forecaster
Explainability
Skforecast in GPU
Plot forecaster residuals
Datasets
Examples and tutorials
Examples and tutorials
Examples and tutorials
API Reference
API Reference
ForecasterAutoreg
ForecasterAutoregCustom
ForecasterAutoregDirect
ForecasterMultiSeries
ForecasterMultiSeriesCustom
ForecasterMultiVariate
ForecasterSarimax
Sarimax
ForecasterBaseline
model_selection
model_selection_multiseries
model_selection_sarimax
preprocessing
plot
utils
datasets
exceptions
FAQ and Tips
FAQ and Tips
Table of contents
Time series differentiation
Avoid negative predictions when forecasting
Forecasting time series with missing values
Cyclical features in time series
Stacking (ensemble) machine learning models
Parallelization in skforecast
Profiling skforecast
Releases
Authors
Table of contents
Libraries
Data
Train and predict using input with datetime and frequency index
Train and predict using input without datetime index