Forecasting and Modeling Monthly Rainfall in Bengaluru, India: An Application of Time Series Models

Hemlata Joshi¹ , Deepa Tyagi²

Section:Research Paper, Product Type: Journal-Paper
Vol.8 , Issue.1 , pp.39-46, Feb-2021

Online published on Feb 28, 2021

Copyright © Hemlata Joshi, Deepa Tyagi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Rainfall is the most essential stochastic phenomenon which plays an important role in Indian agriculture sector and is necessary for economic growth of the country. These days, the prediction of rainfall has become a most challenging task as it is drastically affected by climate changes due to the worsen effects global warming. For the accurate and timely rainfall predictions, in this article seasonal Naive, triple exponential smoothing and seasonal ARIMA time series models have been applied and the comparison of accuracy of forecasts of these time series models has been checked through various scale dependent error forecast methods and the residual analysis. Further, the best fitted time series model for monthly prediction of rainfall of Bengaluru, Karnataka is suggested through the empirical analysis by utilizing the monthly rainfall data recorded from the years 2009 to 2018 of the Bengaluru city of Karnataka State of India and the results show that the seasonal autoregressive moving average model i.e. ARIMA(0,0,2)(1,1,1)_12 provides the more accurate results to forecast the rainfall pattern in Bangalore City of India than the other time series models.

Key-Words / Index Term :
Rainfall Forecasting, Time Series Models, SARIMA, Seasonal Naive, Exponential Smoothing.

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