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題 名 | 水文短序列模式判定與預測之研究=Studies on the Identification and Forecasting for Short Hydrological Time Series |
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作 者 | 虞國興; 金士凱; | 書刊名 | 農業工程學報 |
卷 期 | 45:1 1999.03[民88.03] |
頁 次 | 頁13-23 |
分類號 | 443 |
關鍵詞 | 最大熵法; 動差法; 接近非定常性; Maximum entropy method; Moment method; Close to non-stationarity; |
語 文 | 中文(Chinese) |
中文摘要 | 近來,Hurvich 和Tsai(1997) 針對短時間序列,以最小均方預測誤差建立Z與 {z,...,z �址 之線性關係,提升其預測能力。然其研究僅侷限於某些特殊模式之合成資料 , 故本研究將探討 Hurvich 和 Tsai 所提方法之適用範圍,同時亦針對臺灣河川月流量資 料做一整體性探討。 本研究比較 Hurvich 和 Tsai 所提方法與傳統時間序列模式於定常性 及接近非定常性序列上預測能力之差異, 研究中在 Hurvich 和 Tsai 方法時, 採用 Burg (1978) 與傳統兩種推估自相關變異數之方法推求參數; 而時間序列模式,則採用最大熵與 動差參數推估法推求參數,至於模式之判定採用 AIC �搳BAIC 及 FPE 三種判斷準則。結果 顯示,就合成資料而言,最大熵與 Burg 法,在小樣本情況下,其參數推估精確度皆較動差 法為優;經利用最大熵或 Burg 法推估參數後,AIC �搷P斷準則當模式接近非定常性時有較 佳的偵測能力;於小樣本時,傳統時間序列模式利用最大熵法,其整體預測能力表現較佳, 然樣本個數增加時,Hurvich 和 Tsai 方法與傳統時間序列方法,其預測結果相差不大。就 實測資料之預測結果, 以 Hurvich 和 Tsai 利用傳統自相關變異數推估法之預測能力最優 , 但 SAR 模式與 Hurvich 和 Tsai 利用傳統自相關變異數推估時,兩者預測能力相當, 如考慮參數之精簡原則,則以 SAR 模式較為適用於臺灣河川月流量資料。 |
英文摘要 | Recently, the Hurvich and Tsai (1997) focused on the short time series and made use of the minimizing the mean squared errors to establish the linear relationship between Z and {Z,..., Z �� } 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 provided 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, AIC �� AIC and FPE, are used as the model selection criteria for identifying short time series model. The results of synthetic data show tht maximum entropy and Burg methods have better parameters estimating accuracy than moment method, whatever the data is close to non-staionarity or stationarity with small sample size. After using the mzximum entropy estimator and the autocovariance estimator (Burg) to estimate the parameters, the AIC �� criterion has better quality of identifying model, especially when data closes to nonstationarity. 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 to showd the best forecasting ability for the real data. However, the SAR model and the Hurvich and Tsai have the same qualities of prediction. The SAR model is better for the monthly riverflow data of Taiwan if the principle of parsimony of th eparameter is considerable. �� |
本系統中英文摘要資訊取自各篇刊載內容。