| dc.contributor | 資管系 | - |
| dc.creator (作者) | 楊亨利 | zh_TW |
| dc.creator (作者) | Yang, Heng-Li;Lin, Han-Chou | - |
| dc.date (日期) | 2016-04 | - |
| dc.date.accessioned | 7-Jul-2016 17:03:35 (UTC+8) | - |
| dc.date.available | 7-Jul-2016 17:03:35 (UTC+8) | - |
| dc.date.issued (上傳時間) | 7-Jul-2016 17:03:35 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/98787 | - |
| dc.description.abstract (摘要) | Financial time series forecasting has become a challenge because it is noisy, non-stationary and chaotic. To overcome this limitation, this paper uses empirical mode decomposition (EMD) to aid the financial time series forecasting and proposes an approach via combining ARIMA and SVR (Support Vector Regression) to forecast. The approach contains four steps: (1) using ARIMA to analyze the linear part of the original time series; (2) EMD is used to decompose the dynamics of the non-linear part into several intrinsic mode function (IMF) components and one residual component; (3) developing a SVR model using the above IMFs and residual components as inputs to model the nonlinear part; (4) combining the forecasting results of linear model and nonlinear model. To verify the effectiveness of the proposed approach, four stock indices are chosen as the forecasting targets. Comparing with some existing state-of-the-art models, the proposed approach gives superior results. | - |
| dc.format.extent | 133 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | International Journal on Artificial Intelligence Tools, 25(2), 1650005 | - |
| dc.subject (關鍵詞) | Financial time series forecasting; empirical mode decomposition; intrinsic mode function; ARIMA; support vector regression | - |
| dc.title (題名) | An Integraged Model Combined ARIMA, EMD with SVR for Stock Indices Forecasting | - |
| dc.type (資料類型) | article | - |
| dc.identifier.doi (DOI) | 10.1142/S0218213016500056 | - |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1142/S0218213016500056 | - |
