 Predicting Economic Time Series Using a Nonlinear Deterministic TechniqueLiangyue Cao, Yiguang Hong, Hanzhang Zhao and Shuhui Deng Computational Economics, 1996, vol. 9, issue 2, 149-78 Abstract: In this paper, a deterministic predictive technique is introduced, which is based on the embedding theorem by Takens and the recently developed wavelet networks. Several economic time series are tested by using this technique. As a result, the predicted values correspond quite well with the actual values. It shows that some economic time series are predictable by using a deterministic approach. Furthermore, the effects of using smoothing techniques (e.g., moving average) upon the prediction results are also investigated since there inevitably exists noise in almost all economic time series. Our numerical results show that smoothing like moving average can improve the prediction results for some of our tested time series, and for others predictions without smoothing are even better than with smoothing. This implies that the wavelet network is capable of drawing the underlying dynamics directly from noisy economic time series. Coauthors are Yiguang Hong, Hanzhang Zhao, and Shuhi Deng. Citation Copyright 1996 by Kluwer Academic Publishers. Date: 1996 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:9:y:1996:i:2:p:149-78 Ordering information: This journal article can be ordered from Access Statistics for this article Computational Economics is currently edited by Hans Amman More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC. |
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