Rainfall Forecasting Model Using ARIMA and Kalman Filter in Makassar, Indonesia

Abstract

Many forecasting methods have been used for forecasting rainfall data. Kalman Filter is one of the forecasting methods that could give better forecasts. To our knowledge, the Kalman Filter method has not been used to forecast rainfall data in Makassar, Indonesia. This study aims to provide more precise forecasts for rainfall data in Makassar, Indonesia by using Autoregressive Integrated Moving Average (ARIMA) and Kalman Filter methods. Rainfall data from January 2010 to December 2020 were used. The best model selection is based on the smallest Mean Absolute Percentage Error (MAPE) value. The results showed that the best ARIMA model is ARIMA(0,1,1)(0,1,1)12 with MAPE is 111.48, while MAPE value by using the Kalman Filter algorithm is 47.00 indicating that Kalman Filter has better prediction than ARIMA model.