Sign in
Ctrl K

s2spy

A high-level python package integrating expert knowledge and artificial intelligence to boost sub-seasonal to seasonal (S2S) forecasting.

3
mentions
6
contributors

Cite this software

What s2spy can do for you

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
Logo of s2spy
Keywords
Programming languages
  • Python 99%
  • Shell 1%
License
</>Source code

Participating organisations

Netherlands eScience Center
Vrije Universiteit Amsterdam

Mentions

Contributors

Fakhereh (Sarah) Alidoost
Fakhereh (Sarah) Alidoost
JvI
Jannes van Ingen
Vrije Universiteit Amsterdam
SV
Sem Vijverberg
Yang Liu
Yang Liu
Lead RSE
Netherlands eScience Center

Related projects

AI4S2S

A high-level python package integrating expert knowledge and artificial intelligence to boost (sub) seasonal forecasting

Updated 18 months ago
In progress

Related software

Lilio

LI

A Python package for generating calendars to resample timeseries into training and target data for machine learning. Named after the inventor of the Gregorian Calendar.

Updated 10 months ago
3 5