Regression Model

We consider the problem of predicting total Indian summer monsoon rainfall (ISMR). A popular approach in prior literature [1], [2] has been to fit a regression model with the precipitation as predictand and various climatological indices and parameters as predictors. The predictor climatological indices and parameters are detected through an analysis of their linear correlations with the Indian monsoon precipitation. Due to limited success of such prior work based on a fixed regression model, in this work we investigate ISMR prediction based on the hypothesis that Indian monsoon operates in a few different regimes, where different predictors become relevant and influential. We model such a multi-regime setting as a finite mixture of linear regressions (MLR) model [3], with a ridge regression model for each regime of operation. The parameters of the model are determined using the Expectation Maximization (EM) algorithm. The prediction procedure consists of identifying the regime of operation and then applying the corresponding regression model. The MLR model seems to improve overall prediction accuracy compared to a single fixed regression model (SLR).