Abstract
ABSTRACTThe 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 difference-autoregressive 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.
Highlights
Hydrologic time series is a sample of observations with complex variation characteristics
The WDIAR forecast model which combined with the wavelet decomposition theory, the first order difference theory and the autoregressive modelling method and the difference autoregression (DIAR) forecasting model which combined with the DIAR (1,2) Wavelet-DIAR (1,2) Wavelet-Fictional difference-autoregressive (FIAR) (0.38,2)
The prediction accuracy of the WDIAR forecasting model is better than the DIAR model, but is still far from being satisfactory
Summary
Hydrologic time series is a sample of observations with complex variation characteristics. Establishing an auto-regressive model is a traditional time series prediction method with a convenient and intuitive advantage (Lin, Xia, and Jie, 2013; Wang, Liang, and Ding, 2016). Hydrological time series are usually non-stationary and do not meet the requirements of establishing an auto-regressive model. Hu, Xu, and Wang (2006) proposed a method on the basis of fractional difference Fuzzy-AR method for network traffic simulation and prediction, the fractional difference method was used to eliminate the longtime correlation of the time series. Zuo, Wang, and Xu (2009) established the coefficient auto-regressive predictive model based on fractional difference method, automatically selected fractional-order difference or integerorder difference to deal with the original data and estimate the optimal modelling parameters. The above attempts prove the validity of fractional order difference in processing time series
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