以月、週和日為時間間距的非定常性時間序列常被使用於水文上。週期性水文時間序列之模擬遠較年時間序列復雜，因前者受到了年循環的影響，使得此時間序列之一些或全部統計特性產生週期性的變動。近來，有學者提出所謂的部份自迴歸模式。在這些模式中，一些自迴歸模式之參數被設定為零。由Haggan 和Oyetunj i (1984 )提出選擇最佳部份自迴歸模式的方法，是根據所有可能部份模式殘差的變異數為準則。然而，當自迴歸過程之階級大時，他們的方法就無效率了。 本論文建議藉倒自相關函數( Inverse Autocorrelation Function) 選擇模式中顯著項的方法來適合部份自迴歸模式於週期性水文時間序列。此方法使用在合成資料上已證明它的可行性，且在週期性水文時間序列之應用上亦顯示出它的效率性。 Nonstationary series of time intervals such as monthes, weeks and days are most often used in. hydrology. The modeling of periodic hydrologic series is more complex than that of annual series because the former has the influence of the annual cycle which produces periodic variations in some or all of the statistical characteristics of the series. Recently, a class of models called subset autoregressive models have been proposed. In these models, some parameters of autoregressive model are restricted to be zero. The method proposed by Uaggan and Oyetunji (1984) for selecting the best autoregressive model is based on the residual variance of all possible subset models. However, their method is not efficient when the order of autoregressive process is large. In this paper, a method by employin.([ the inverse autocorrelation, functioa to select the significant lags of model is suggested. for fitting subset autoregressive model to the periodic time series. This method is used on, synthetic. data to assess it performance. The application of this method to periodic hydrologic time series are also presented. to demo'nstrate its effectivenesa.
關聯:
第四屆水利工程研討會論文集=Proceedings of 4th Conference on Hydraulic Engineering，頁179-187
