Performance improvement via bagging in probabilistic prediction of chaotic time series using similarity of attractors and LOOCV predictable horizon-Reference-Cited by-同舟云学术

Performance improvement via bagging in probabilistic prediction of chaotic time series using similarity of attractors and LOOCV predictable horizon

Published:2017-07-15 Issue:9 Volume:29 Page:341-349
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Kurogi Shuichi^ORCID,Toidani Mitsuki,Shigematsu Ryosuke,Matsuo Kazuya

Funder

ICONIP2015

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/article/10.1007/s00521-017-3149-7/fulltext.html

Reference13 articles.

1. Aihara K (2000) Theories and applications of chaotic time series analysis. Sangyo Tosho, Tokyo

2. Lendasse A, Oja E (2004) Time series prediction competition: the cats benchmark. Proc IJCNN 2004:1615–1620

3. Kurogi S, Ueno T, Sawa M (2007) Time series prediction of the CATS benchmark using Fourier bandpass filters and competitive associative nets. Neurocomputing 70(13–15):2354–2362

4. Kurogi S, Tanaka S, Koyama R (2007) Combining the predictions of a time series and the first-order difference using bagging of competitive associative nets. In: Proceedings of the European symposium on time series prediction (ESTSP) 2007, pp 123–131

5. Kurogi S, Ono K, Nishida T (2013) Experimental analysis of moments of predictive deviations as ensemble diversity measures for model selection in time series prediction. In: Proceedings of ICONIP, (2013) Part III, LNCS 8228. Springer, Heidelberg

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