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| Field |
Value |
| Title: | Identification environment and robust forecasting for nonlinear time series |
| Authors: | 吳柏林 |
| Keywords: | nonlinear time series;bilinear;Lagrange multiplier test;neural network;forecasting;robust |
| Date: | 1994-02 |
| Issue Date: | 2008-12-24 13:38:42 (UTC+8) |
| Abstract: | In this paper, the methods of time series for nonlinearity are briefly surveyed, with particular attention paid to a new test design based on a neural network specification. The proposed integrated expert system contains two main components: an identification environment and a robust forecasting design. The identification environment can be viewed as a integrated dynamic design in which cognitive capabilities arise as a direct consequence of their self-organizational properties. The integrated framework used for discussing the similarities and differences in the nonlinear time series behavior is presented. Moreover, its performance in prediction proves to be superior than the former work. For the investigation of robust forecasting, we perform a simulation study to demonstrate the applicability and the forecasting performance. |
| Relation: | Computational Economics,7(1),37-53 |
| Data Type: | article |
| DOI: | http://dx.doi.org/10.1007/BF01299328 |
| DCField |
Value |
Language |
| dc.creator (Authors) | 吳柏林 | zh_TW |
| dc.date (Date) | 1994-02 | en_US |
| dc.date.accessioned | 2008-12-24 13:38:42 (UTC+8) | - |
| dc.date.available | 2008-12-24 13:38:42 (UTC+8) | - |
| dc.date.issued (Issue Date) | 2008-12-24 13:38:42 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/18837 | - |
| dc.description.abstract (Abstract) | In this paper, the methods of time series for nonlinearity are briefly surveyed, with particular attention paid to a new test design based on a neural network specification. The proposed integrated expert system contains two main components: an identification environment and a robust forecasting design. The identification environment can be viewed as a integrated dynamic design in which cognitive capabilities arise as a direct consequence of their self-organizational properties. The integrated framework used for discussing the similarities and differences in the nonlinear time series behavior is presented. Moreover, its performance in prediction proves to be superior than the former work. For the investigation of robust forecasting, we perform a simulation study to demonstrate the applicability and the forecasting performance. | en_US |
| dc.format | application/ | en_US |
| dc.language (Language) | en | en_US |
| dc.language (Language) | en-US | en_US |
| dc.language.iso | en_US | - |
| dc.relation (Relation) | Computational Economics,7(1),37-53 | en_US |
| dc.subject (Keywords) | nonlinear time series;bilinear;Lagrange multiplier test;neural network;forecasting;robust | en_US |
| dc.title (Title) | Identification environment and robust forecasting for nonlinear time series | en_US |
| dc.type (Data Type) | article | en |
| dc.identifier.doi (DOI) | 10.1007/BF01299328 | - |
| dc.doi.uri | http://dx.doi.org/10.1007/BF01299328 | - |
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