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    題名: Applying the Hybrid Model of EMD, PSR, and ELM to Exchange Rates Forecasting
    作者: 楊亨利
    Yang, Heng-Li
    Lin, Han-Chou
    貢獻者: 資訊管理系
    關鍵詞: Exchange Rate
    日期: 2017-01
    上傳時間: 2017-08-30 16:07:31 (UTC+8)
    摘要: Financial time series forecasting has been a challenge for time series analysts and researchers because it is noisy, nonstationary and chaotic. To overcome this limitation, this study uses empirical mode decomposition (EMD) and phase space reconstruction (PSR) to assist in the task of financial time series forecasting. In addition, we propose an approach that combines these two data preprocessing methods with extreme learning machine (ELM). The approach contains four steps as follows. (1) EMD is used to decompose the dynamics of the exchange rate time series into several components of intrinsic mode function (IMF) and one residual component. (2) The IMF and residual time series phase space is reconstructed to reveal its unseen dynamics according to the optimum time delay tau and embedding dimension m. (3) The reconstructed time series datasets are divided into two datasets: training and testing, in which the training datasets are used to build ELM models. (4) A regression forecast model is set up for each IMF as well as the residual component by using ELM. The final prediction results are obtained by compositing the prediction values. To verify the effectiveness of the proposed approach, four exchange rates are chosen as the forecasting targets. Compared with some existing state-of-the-art models, the proposed approach yields superior results. Academically, we demonstrated the validity and superiority of the proposed approach that integrates EMD, PSR, and ELM. Corporations or individuals can apply the results of this study to acquire accurate exchange rate information and reduce exchange rate expenses.
    關聯: Computational Economics, v. 49, iss. 1, pp. 99-116
    資料類型: article
    DOI 連結: http://dx.doi.org/10.1007/s10614-015-9549-9
    DOI: 10.1007/s10614-015-9549-9
    顯示於類別:[財政學系] 期刊論文


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