Comparative Analysis of Time Series Forecasting Models to Predict Amount of Rainfall in Telangana

Abstract

India lies in the tropical wet and dry area and gets a huge amount of rainwater in the monsoon season accounting for most of the yearly rainfall. Predicting the rainfall in these types of tropical areas is a difficult task and moreover, the influence of environmental issues has resulted in a substantial rise in the average rainfall distribution as well as the severity and frequency. In this work, for predicting the rainfall various Time series forecasting models like Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt's Winter Seasonal Method (HWSM), Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH), and Holt's Linear Trend Method (HLTM) have been used. Finally, a comparative analysis was made to inspect the best time-series forecasting model. The evaluation parameters used to perform a comparative analysis of different Time Series forecasting models are RMSE (Root Mean Square Error), Mean Absolute Error (MAE), and Mean Square Error (MSE). It was observed that the HWSM model had recorded the least error rate of 5.767 and 5.343 as RMSE, 2.143 and 2.432 as MAE, 31.65 and 26.75 as MSE for dataset-1 and dataset-2 respectively when compared to the remaining models.