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

Abstract

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.Our results showed that the best predictive performance was obtained when we use the signature method with the LSTM (R2 = 0.74) and Lasso (R2 = 0.79) models. Two complementary methods were used to highlight the influence of the following climate indices on ENSO cycle changes: NINO3, NINO3.4 and NPI (North Pacific Index). These methodologies also enabled us to determine the switchover thresholds, and order temporal variations. With this first application of the signature method to this type of time series, we obtained accurate forecasts on a 6-month scale with reduced computation time. This suggests that our methodology can be applied to many other fields of research that use multivariate time-series analyses.