Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/18837


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 連結: http://dx.doi.org/10.1007/BF01299328
Appears in Collections:[應用數學系] 期刊論文

Files in This Item:

File SizeFormat
37-53.pdf891KbAdobe PDF485View/Open


All items in 學術集成 are protected by copyright, with all rights reserved.


社群 sharing