Using Echo State Network based on Ridge Regression for Time Series Forecasting
Raed Arif Snaan, Osamah Basheer Shukur · Iraqi statisticians journal. · 2025
Studying climate in terms of predicting evaporation quantities through changing many factors and climate variables is of great importance to reduce the risks of climate change and its impact on many environmental phenomena affecting human and plant health. Weather patterns and conditions will be analyzed for a limited period of time. In this study, Ridge Regression (RR) was used to identify components, address the problem of multicollinearity and multicollinearity, and overcome the problem of multicollinearity of the reconstructed inputs. The problem of multicollinearity occurs when building a multiple linear regression model, i.e. the presence of a strong correlation between the predictive variables, and this correlation leads to an increase in the variance of the regression parameter estimates and obtaining unstable estimates. Forecasting chaotic time series represented by climate data affecting the evaporation time series often suffers from the problem of nonlinearity in addition to multicollinearity. The Echo State Network (ESN) is a neural network specialized in predicting time series after addressing the problem of synchronization with the time variable as a recurrent network to address time-dependent effects and accurate prediction of time series in addition to nonlinear modeling. RR provides accurate predictions due to its structural effect to avoid overfitting while ESN will address the nonlinearity problem in addition to the problems addressed by RR model which ESN structure is based on. The expected results show that ESN model based on RR significantly outperforms traditional models in multivariate time series forecasting.