Chap already contains rich functionality for data handling, data input from DHIS2 and rigorous model evaluation. This allows modellers to focus only on training and predicting based on a single provided dataset, while relying on the Chap framework to collect data from various sources, parse data of different formats, and perform multiple train-and-predict iterations as part of a rigorous time-series cross-validation.
If you specify max_prediction_length in your model configuration, Chap can automatically extend your model’s prediction horizon using ExtendedPredictor. This wrapper makes predictions beyond your model’s limit through iterative prediction: it predicts up to the maximum length, adds those predictions to the historic data, then predicts again. This repeats until the desired forecast length is reached.
There are ongoing developments for a range of further supporting features, allowing modellers to rely on the Chap framework for model tuning (autoML), ensemble model learning, model explainability and more.