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| Title: | 小波分析方法對時間序列模型預測能力之影響 -以新台幣對美元匯率為例
The Influence of Time Series model Forecasting Accuracy On Wavelet Analysis -Evidence from NTD/USD exchange rate |
| Authors: | 吳修宏
Wu, Hsiu Hung |
| Contributors: | 廖四郎
Liao, Szu Lang
吳修宏
Wu, Hsiu Hung |
| Keywords: | 小波分析
預測
匯率
時間序列
wavelet transform
forecasting
exchange rate
time series |
| Date: | 2012 |
| Issue Date: | 2014-07-01 12:06:42 (UTC+8) |
| Abstract: | 過去對匯率資料進行預測研究，多數利用時間序列模型、迴歸模型等方法，為了提升預測能力，學者們著重於迴歸因子的選取、模型參數的配適、假設條件的放寬或者模型的改進等等，已經成為一個龐大的結構。近年來新的預測方法興起，開始有類神經模擬、基因演算法等人工智慧方法可對匯率、股價等金融商品時間序列資料進行預測。
當有如此多的預測模型可供選擇，為了提升模型的預測效果，開始有研究在使用預測模型對資料進行預測之前先對資料進行分析及處理。本研究所使用之小波轉換方法為近年來常被搭配使用之訊號分析方法，藉由小波分解將資料分解成較為低頻的近似子序列及高頻的細部子序列，如同時間序列資料的長期趨勢項及短期波動項，而後再將兩項不同特性的序列分別用時間序列模型進行預測，時間序列模型選擇較常被使用的ARMA模型及ARMA-GARCH模型，經由配適後的模型得到預測值後再將兩者進行合成，還原成匯率的預測值，並且跟實際值進行比較。
研究結果發現加入小波轉換對資料先進行處理後再使用時間序列模型預測，透過MAE及RMSE預測力指標的判斷下能夠有效提升模型對於日資料、周資料及月資料的預測能力，也就是不論資料周期長短期小波轉換都有其功用存在。
This paper illustrates an application of wavelets transform method with “singal analysis methods”. The entire procedure can be roughly divided into three steps: wavelet decomposition, signal extension and wavelet reconstruction. In the step of wavelet decomposition, we divide the data into low-signal and high-signal time-series sub data just like the long term trend and the short term volatility in time-series. Second, we apply the ARMA and ARMA-GARCH model to forecast the exchange rate separately and finally reconstruct the two predicting value from the best fitting model to form the forecasting exchange rate which could be compared to the real value.
It could be concluded in this study that if we apply the MAE and RMSE index to evaluate the predicting result which is generated from the time-series model with the wavelets transformation of the data beforehand, the forecasting accuracy could be enhanced no matter the data are in daily, weekly or monthly type. In other words, no matter what type of time series data is, the wavelets transform method does enhance the forecasting accuracy. |
| Reference: | 中文
呂佳芹(2009)，應用時間序列、演化式類神經網路與灰預測方法在匯率預測績效之比較，朝陽科技大學財務金融所碩士論文。
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簡苺蓉(2007)，小波轉換結合類神經網路匯率預測能力之研究，高雄應用科技大學商務經營研究所碩士論文。
英文
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| Description: | 碩士
國立政治大學
金融研究所
100352027
101 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0100352027 |
| Data Type: | thesis |
| Appears in Collections: | [金融學系] 學位論文
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