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| Title: | 評估不同模型在樣本外的預測能力
利用支向機來做預測的結合 |
| Authors: | 蔡欣民
Tsai Shin-Ming |
| Contributors: | 陳樹衡
Chen Shu-Heng
蔡欣民
Tsai Shin-Ming |
| Keywords: | 預測結合
樣本外的預測
預測誤差的範圍
支向機
時間序列模型
Combined Forecast
Out-of-sample Forecast
Range of Forecast Error
Support Vector Machine
Time Series Models |
| Date: | 2003 |
| Issue Date: | 2009-09-14 13:25:05 (UTC+8) |
| Abstract: | 明天股票的價格是會漲還是會跌呢?
明天到底會不會下雨?
下期樂透開獎會是哪些號碼呢?
未來不知道會發生哪些事情?
大家總是希望能夠未卜先知、洞悉未來!
可是我們要如何進行預測呢?
本文比較了不同時間序列模型的預測績效,
而且測試預測的結合是否能夠改進預測的準確度?
時間序列模型的研究在近年來非常蓬勃地發展,
所以本文簡單介紹了時間序列模型(Time series models)當中的線性AR模型、非線性TAR模型、非線性STAR模型,
以及這些模型該如何來進行在樣本外的預測。
同時本文說明了預測的結合(Combined forecast)該如何進行?
預測結合的目的是希望能夠達到截長補短的效果!
除了傳統迴歸(Regression-based)方法和變動係數(Time-varying coefficients)方法外,
本文提出了兩種非迴歸類型的預測結合方法,
績效權數(Fitness weight)和支向機(Support Vector Machine)。
其中主要的焦點放在支向機,
因為迴歸方法可能會有共線性的問題,
支向機則是沒有這個問題。
本文實證的結果顯示,
在時間序列模型方面,
非線性模型的預測能力, 在大多數的情形底下, 都不如簡單的線性AR模型;
在預測結合的方面,
支向機的績效是和迴歸方法的績效是差不多的, 這兩者都比變動係數方法的績效來得穩固,
可是如果基底模型的預測值存在共線性的問題或樣本數目過少的問題,
那麼支向機的績效是優於迴歸方法的績效。
最後, 時間序列模型的預測績效會受到資料性質的影響, 而有極大的改變,
或許我們可以考慮使用比較保險的預測策略-預測結合,
因為預測結合的預測誤差範圍是小於時間序列模型的預測誤差範圍! |
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| Description: | 碩士
國立政治大學
經濟研究所
90258017
92 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0090258017 |
| Data Type: | thesis |
| Appears in Collections: | [經濟學系] 學位論文
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