Abstract
Time series analysis of rainfall is very much essential for farming. Agriculture productivity is depended on rainfall. It is important to predict the future rainfall from farmers’ point of view. In this paper, we apply seasonal auto-regressive integrated moving average (SARIMA) and auto-regressive integrated moving average (ARIMA) techniques for the monthly time series analysis of rainfall in Assam. The rainfall data contains the monthly rainfall of Assam from 1901 to 2017. Here, different components of the rainfall are visualized before apply the SARIMA and ARIMA. The handling procedures of seasonal components (p, d, and q) are reported using moving average, and augmented ducky fuller test. The ACF and PACF are used to find the seasonal components of the SARIMA and ARIMA. The SARIMA model is selected as the best model as compared to ARIMA based on AIC, BIC, HQIC, regression score (RC), mean absolute error (MAE), median absolute error (MeAE), mean squared error (MSE), mean squared log error (MSLE), and root mean square error (RMSE) of the analysis. The final results of the two methods are validated with the actual rainfall of Assam during the period.
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Acknowledgements
This research project is supported by Assam Science and Technology University, Guwahati, Assam under TEQIP-III, vide Ref. No.: ASTU/TEQIP-III/Collaborative Research/2019/2474, Dated July 17, 2019.
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Barman, U., Hussain, A.E., Dahal, M.J., Barman, P., Hazarika, M. (2021). Time Series Analysis of Assam Rainfall Using SARIMA and ARIMA. In: Satapathy, S.C., Bhateja, V., Favorskaya, M.N., Adilakshmi, T. (eds) Smart Computing Techniques and Applications. Smart Innovation, Systems and Technologies, vol 225. Springer, Singapore. https://doi.org/10.1007/978-981-16-0878-0_35
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DOI: https://doi.org/10.1007/978-981-16-0878-0_35
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