Getting Started¶
Start here. These tutorials cover the core Yohou workflow from a first forecast to walk-forward evaluation, reduction strategies, and working with exogenous features. Each notebook is self-contained and builds on the previous.
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How to Choose a Forecasting Method
Interactive decision guide progressing from SeasonalNaive baseline through linear reduction, stationarity transforms, feature enrichment, nonlinear models, decomposition, and prediction intervals.
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Class-Probability Forecasting
Forecast air quality categories using ClassProbaReductionForecaster, producing a probability distribution over four WHO air quality classes.
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Conformal Prediction Intervals
Build distribution-free prediction intervals with SplitConformalForecaster using calibration holdouts and configurable conformity scoring functions.
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How to Create a Custom Class-Probability Forecaster
Implement a MajorityClassForecaster from scratch, validate it with the check generator, and compare it against ClassProbaReductionForecaster.
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How to Create a Custom Estimator
Implement a LastValueForecaster from scratch, validate it with the check generator, and use it in a forecast pipeline.
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How to Create a Custom Interval Forecaster
Implement a NaiveIntervalForecaster from scratch, validate it with the check generator, and compare it against SplitConformalForecaster.
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How to Create a Custom Transformer
Implement a ScaleTransformer from scratch, validate it with the check generator, and use it in a forecast pipeline.
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CV Splitters
Demonstrate ExpandingWindowSplitter and SlidingWindowSplitter for temporal cross-validation with configurable test_size, stride, and fold visualisation.
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Exogenous Features (X_actual, X_future, X_forecast)
Build a forecasting model with actual observations, known-future indicators, and multi-vintage external forecasts on synthetic electricity price data.
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Forecasting Workflow
Evaluate forecasters with cross-validation, search hyperparameters with GridSearchCV, and inspect residuals to diagnose model weaknesses.
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Interval Forecasting
Wrap a point forecaster with SplitConformalForecaster to produce 95% prediction intervals with statistical coverage guarantees.
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Naive Forecasters
Baseline forecasting (the first portion of the First Forecast tutorial) with SeasonalNaive using different seasonality periods, the observe/predict streaming workflow, and rolling evaluation patterns.
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Observe-Predict Workflow
Walk through a test set in batches, updating forecasts as new data arrives with observe_predict.
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Panel Data Forecasting
Forecast multiple related time series simultaneously using the __ naming convention, LocalPanelForecaster, and per-group scoring.
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Reduction Forecasting Walkthrough
Walk through the full fit/predict/evaluate cycle with PointReductionForecaster, cross-validation, and grid search on a real dataset.
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Direct, Recursive, and MIMO Strategies
Compare direct, recursive, and MIMO reduction strategies across forecasting horizons to understand the trade-offs for your use case.
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How to Save and Load Forecasters
Serialize fitted forecasters with joblib and pickle, reload them in a fresh session, and produce predictions without retraining.
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Quickstart
Comprehensive end-to-end tour of yohou beyond the Getting Started tutorials, covering data loading, baseline forecasting, preprocessing pipelines, decomposition, cross-validation search, and interval prediction.