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Skforecast Docs
GitHub
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Home
Welcome to skforecast
Intro to Forecasting
Quick start
Quick start
Quick start
Forecaster Parameters
Forecaster Attributes
How to install
User Guides
User Guides
Table of contents
Skforecast 0.14 Migration guide
Input data
Single series Forecasters
Single series Forecasters
Recursive multi-step forecasting
Direct multi-step forecasting
ARIMA and SARIMAX forecasting
Forecasting baseline
Global Forecasters (multiple series)
Global Forecasters (multiple series)
Independent multi-time series forecasting
Series with different lengths and different exogenous variables
Dependent multivariate series forecasting
Deep learning Recurrent Neural Networks
Feature Engineering
Feature Engineering
Exogenous variables
Window and custom features
Categorical features
Categorical features
Table of contents
Libraries and data
One Hot Encoding
Ordinal encoding
Target encoding
Native implementation for categorical features
LightGBM
Scikit-learn HistogramGradientBoosting
XGBoost
CatBoost
Calendars features
Data transformation
Differentiation
Feature selection
Sktime pipelines
Model Evaluation and Tuning
Model Evaluation and Tuning
Metrics
Backtesting forecaster
Hyperparameter tuning and lags selection
Feature selection
Probabilistic Forecasting
Probabilistic Forecasting
Overview
Bootstrapped residuals
Conformal predictions
Conformal calibration
Quantile forecasting
Probabilistic global models
Metrics in probabilistic forecasting
Continuous Ranked Probability Score (CRPS)
Model Explainability
Model deployment
Model deployment
Save and load forecaster
Forecaster in production
Plotting
Datasets
Additional Resources
Additional Resources
Extract training and prediction matrices
Weighted time series forecasting
Stacking multiple models
Forecasting with XGBoost and LightGBM
Skforecast in GPU
FAQ and forecasting tips
FAQ and forecasting tips
Avoid negative predictions when forecasting
Forecasting time series with missing values
Forecasting with delayed historical data
Backtesting vs One-step-ahead
Continuous Ranked Probability Score (CRPS)
Calibration of probabilistic forecasting intervals
Cyclical features in time series
Time series aggregation
Parallelization in skforecast
Profiling skforecast
Examples and tutorials
Examples and tutorials
English
Spanish
API Reference
API Reference
recursive
recursive
ForecasterRecursive
ForecasterRecursiveMultiSeries
ForecasterSarimax
ForecasterEquivalentDate
direct
direct
ForecasterDirect
ForecasterDirectMultiVariate
deep_learning
deep_learning
ForecasterRnn
sarimax
model_selection
feature_selection
preprocessing
metrics
plot
utils
datasets
exceptions
FAQ and Tips
FAQ and Tips
Table of contents
Avoid negative predictions when forecasting
Forecasting time series with missing values
Forecasting with delayed historical data
Backtesting vs One-step-ahead
Cyclical features in time series
Time series aggregation
Continuous Ranked Probability Score (CRPS)
Calibration of probabilistic forecasting intervals
Parallelization in skforecast
Profiling skforecast
Releases
More
More
About skforecast
Consulting & Professional services
Funding