Improved climate time series forecasts by machine learning and statistical models coupled with signature method: A case study with El Niño

The different phases of ENSO (El Niño Southern Oscillation) directly influence the occurrence of natural disasters and global warming. To limit the socio-economic impact, it is essential to develop simple and fast numerical models that can predict these different cycles. Here, we aimed to improve the predictive performance and extracting relevant information from climatic events by applying the signature method to time series models. After transforming the data using this signature method, we performed a comparative analysis of the statistical and machine learning models. In addition, we used PDP (Partial Dependence Plot) and SPRC (Standard Partial Regression Coefficient) to better understand the interactions between different climate indices.