Time series modeling using markov and arima models

Abstract

Streamflow forecasting plays important roles for flood mitigation and water resources allocation and management. Inaccurate forecasting will cause losses to water resources managers and users. The suitability of forecasting method depends on type and number of available data. Thus, the objective of this study are to propose the streamflow forecasting methods using Markov and ARIMA models and to inspect the accuracy of Markov and ARIMA models in forecasting ability. Streamflow data of Sungai Bernam, Selangor was used. Minitab and Microsoft Excel were used to model ARIMA and Markov respectively. Criteria performance evaluation procedure that being used in this study were Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and Chi-square test of Normality to inspect the forecasting accuracy of the different models. The tentative model that best fits the criteria and meets the requirement for ARIMA model is ARIMA (1,1,1)(0,1,1)12. From the criteria performance evaluation procedure, ARIMA model has better performance of model for forecasting than Markov model in this study. Therefore, ARIMA model has the ability to accurately predict the future monthly streamflow for Sungai Bernam.