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Tutorials

Step-by-step guides that teach you the fundamentals of time series forecasting with Yohou.

Reading order

Start here: Getting Started. This tutorial gives you the foundation for everything else.

Then pick a path based on your interest:

Panel Data and Cross-Validation Splitters assume familiarity with the core pipeline.

Tutorial What you will learn
Getting Started Install Yohou, load a dataset, build a full forecasting pipeline, and evaluate multiple models (continuous target)
Class-Probability Forecasting Forecast categorical outcomes as a probability distribution over classes (categorical target, probability output)
Interval Forecasting Produce prediction intervals with statistical coverage guarantees (continuous target, prediction intervals)
Forecasting Workflow Cross-validation, hyperparameter search, and residual diagnostics (continuous target)
Observe/Predict Workflow Step through a test set in batches using the observe/predict loop (continuous target)
Exogenous Features Incorporate external data (X_actual, X_future, X_forecast) into your models (continuous target)
Panel Data Forecast multiple related time series simultaneously using the __ naming convention and LocalPanelForecaster
Cross-Validation Splitters Create temporal train/test folds with expanding and sliding window strategies
Reduction Strategies Compare multi-output, direct, and dir-rec reduction strategies
Decomposition Build a DecompositionPipeline with trend, seasonality, and residual forecasters
Exploratory Visualization Plot rolling statistics, boxplots, missing data, outliers, and resampling comparisons
Forecast Visualization Visualize single and multi-model forecasts, intervals, decomposition, and time weights
Seasonal Analysis Analyze seasonality with overlays, ACF/PACF, STL decomposition, and heatmaps