![]() The findings of the study suggest that the performance of SARIMA models can be enhanced by using appropriate transformation (Box–Cox) along with GARCH model of residuals of highly skewed rainfall time series from both climatic environments. The hybrid SARIMA–GARCH model based on transformed rainfall time series resulted in good statistics performance indices at both climatic environments. Then, the rainfall time series was transformed (differencing and Box–Cox) so that the effect of heteroscedasticity is eliminated. The residuals from SARIMA models were tested for heteroscedasticity, utilizing the McLeod–Li test, and demonstrated some autocorrelation. In addition, the effectiveness of data normalization techniques (differencing and transformation) to stabilize conditional variance in the SARIMA residuals is additionally examined. In this paper, a hybrid of seasonal autoregressive integrated moving average (SARIMA)-generalized autoregressive conditional heteroscedasticity (GARCH) was applied to eliminate conditional variance of the SARIMA model of rainfall time series in two different climatic environments (Agartala: humid, and Jodhpur: arid). ![]()
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