s2spy

Producing reliable sub-seasonal to seasonal (S2S) forecasts with machine learning techniques remains a challenge. Currently, these data-driven S2S forecasts generally suffer from a lack of trust because of:

• Intransparent data processing and poorly reproducible scientific outcomes
• Technical pitfalls related to machine learning-based predictability (e.g. overfitting)
• Black-box methods without sufficient explanation

To tackle these challenges, we build s2spy which is an open-source, high-level python package. It provides an interface between artificial intelligence and expert knowledge, to boost predictability and physical understanding of S2S processes.

It can facilitate your data-driven forecasting workflow with:

• Datetime operations & data processing
• Preprocessing
• Dimensionality reduction
• Cross-validation
• Model training
• Explainable AI analysis