Performance of Combined Double Seasonal Univariate Time Series Models for Forecasting Water Demand

This paper examines the daily water demand forecasting performance of double seasonal univariate time series models (Holt-Winters, ARIMA, and GARCH) based on multistep ahead forecast mean squared errors. A within-week seasonal cycle and a within-year seasonal cycle are accommodated in the various model specifications to capture both seasonalities. The study investigates whether combining forecasts from different methods could improve forecast accuracy. The results suggest that the combined forecasts perform quite well, especially for short-term forecasting. On the other hand, the individual forecasts from Holt-Winters exponential smoothing and GARCH models can improve forecast accuracy on specific days of the week.