Establishment and application of a fractional difference-autoregressive model for daily runoff time series forecasting based on wavelet analysis

Abstract

The daily runoff is a complicated hydrological time-series, its extreme changes will affect not only the safety of the ships underway but also the dispatching of water transportation. Daily runoff forecast is a hot topic in hydrologic analysis and water transport planning. The fractional differenceautoregressive model based on wavelet analysis (WFIAR) is used to forecast the daily runoff. The daily runoff time series from 2002 to 2013 of Hankou Hydrologic Station are decomposed by the orthogonal db4 wavelet function into a series of stationary high-frequency sub-sequences and a non-stationary low frequency subsequence. According to the Hurst exponent of the low frequency subsequence, the appropriate difference order is determined and fractional difference is made. The differenced low-frequency part and high-frequency parts set up autoregressive models separately. These models are combined to forecast the daily runoff time series in 2014 and 2015 separately. The first-order difference autoregression (DIAR) model and the first-order autoregression (WDIAR) model based on wavelet analysis are also established to compare the forecasting accuracy of different models. The results show that the prediction accuracy of WFIAR is higher than the other two models that reveal the advantage of WDIAR model in daily runoff forecast. ARTICLE HISTORY