SARIMA Modelling and Forecasting of Monthly Rainfall Patterns for Coimbatore, Tamil Nadu, India
S. Kokilavani, R. Pangayarselvi, S. P. Ramanathan, Ga. Dheebakaran, N. K. Sathyamoorthy, N. Maragatham, R. Gowtham
Current Journal of Applied Science and Technology · pp. 69–76 · Published 5 May 2020
10.9734/cjast/2020/v39i830594Abstract
Weather forecasting is an important subject in the field of meteorology all over the world. The pattern and amount of rainfall are the essential factors that affect agricultural systems. The present paper describes an empirical study for modeling and forecasting the time series of monthly rainfall patterns for Coimbatore, Tamil Nadu. The Box-Jenkins Seasonal Autoregressive Integrated Moving Average (SARIMA) methodology has been adopted for model identification, diagnostic checking and forecasting for this region. The best SARIMA models were selected based on the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) and the minimum values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The study has shown that the SARIMA (0,0,0)(2,0,0)12 model was appropriate for analysing and forecasting the future rainfall patterns. The Root Means Square Error (RMSE) values were found to be 52.37 and proved that the above model was the best model for further forecasting the rainfall.
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