結合經驗組態分解與最小平方支援向量迴歸之匯率預測模式

Abstract

由於財務時間序列資料具有高頻率，雜訊，非線性，非穩態與混沌等性質，便無法準確的分析資料並進行預測，使得其在時間序列預測領域中，向來被認為是一極具挑戰性的應用領域。本研究提出一結合經驗模態分解(Empirical Mode Decomposition, EMD)與最小平方支援向量迴歸(Least square support vector regression, LSSVR)之匯率預測模式。由於匯率時間序列的往往無法由觀察到的混合數列中獲得相關的資訊，因此經驗模式分解被發展出來處理非穩態和非線性資料處理上，將匯率時間序列分解成數個本質模態函數（Intrinsic Mode Function, IMF），IMF包含匯率時間序列中不同尺度的特性，能夠充分表達匯率時間序列中所包含之資訊內涵，再使用最小平方支援向量迴歸以所分解出的本質模態函數與一個趨勢函數(Residual component)分別建構預測模式，最後根據分解與整合概念，將所個別建構的預測模型加以整合為總體匯率預測模型。本研究預期所提出整合經驗模態分解與最小平方支援向量迴歸的總體匯率預測模式相對於其他三種模式(EMD-ARIMA, LSSVR, and ARIMA)有較好的預測結果，因此，能提供未來欲進行匯率預測者有效地建構匯率預測模式。

To address the nonlinear and non-stationary characteristics of financial time series such as foreign exchange rates, this study proposes a hybrid forecasting model using empirical mode decomposition (EMD) and least squares support vector regression (LSSVR) for foreign exchange rate forecasting. EMD is used to decompose the dynamics of foreign exchange rate into several intrinsic mode function (IMF) components and one residual component. LSSVR is constructed to forecast these IMFs and residual value individually, and then all these forecasted values are aggregated to produce the final forecasted value for foreign exchange rates. We will expect the empirical results from this paper show that the proposed EMD-LSSVR model outperforms the EMD-ARIMA (autoregressive integrated moving average) as well as the LSSVR and ARIMA models without time series decomposition.