Studies on the Identificasting and Forecasting for Short Hydrological Time Series

• Main Authors: King, Shih-Kai, 金士凱
• Other Authors: Yu Gwo-Hsing
• Format: Others
• Language: zh-TW
• Published: 1998
• Online Access: http://ndltd.ncl.edu.tw/handle/42067685153500512298

碩士 === 淡江大學 === 水資源及環境工程學系 === 86 === Recently, the Hurvich and Tsai (1997) focused on the short time series and made use of the minimizing the mean squared error to establish the linear relationship between Zt+h and {Zt-k+1,..., Zt} in order to increase the forecasting abilities. However, the Hurvich and Tsai''s research only limited on analyzing the synthetic data of some special models. Therefore, the following study is not only probing into the suitable range for the method that Hurvich and Tsai provide but also will focus on the monthly riverflow discharge data of Taiwan to make a comprehensive research.This study uses Burg (1978) and the traditional methods that estimate the autocovariance to yield the estimates of the predictor coefficients from Hurvich and Tsai. In the mean time, this research adopts the maximum entropy and moment methods to estimate the parameters of the time series model when the difference display on the forecasting abilities between the Hurvich and Tsai and the time series model. Three criteria, AICC, AIC and FPE, are used as the model selection criteria for identifying short time series model.The results of synthetic data show that maximum entropy method has better parameters estimating accuracy than moment method, whatever the data is close to non-staionarity or stationarity with small sample size. After using the maximum entropy estimator and the autocovariance estimator (Burg) to estimate the parameters, the AICC criterion has better quality of identifying model, especially when data closes to non-stationarity. For the small sample, the forecasting ability is better when the time series model is used for examining the maximum entropy estimator. When the sample size increases, the forecasting ability of both methods of Hurvich and Tsai and time series models are very similar.In general, the Hurvich and Tsai used the traditional method that estimate the autocovariance showed the best forecasting ability for the real data. However, the SAR model and the Hurvich and Tsai have the same qualities of predication. The SAR model is better for the monthly riverflow data of Taiwan if the principle of parsimony of the parameter is considerable.