Evolving nearest neighbor time series forecasters
- 146 Downloads
This article proposes a nearest neighbors—differential evolution (NNDE) short-term forecasting technique. The values for the parameters time delay \(\tau \), embedding dimension m, and neighborhood size \(\epsilon \), for nearest neighbors forecasting, are optimized using differential evolution. The advantages of nearest neighbors with respect to popular approaches such as ARIMA and artificial neural networks are the capability of dealing properly with nonlinear and chaotic time series. We propose an optimization scheme based on differential evolution for finding a good approximation to the optimal parameter values. Our optimized nearest neighbors method is compared with its deterministic version, demonstrating superior performance with respect to it and the classical algorithms; this comparison is performed using a set of four synthetic chaotic time series and four market stocks time series. We also tested NNDE in noisy scenarios, where deterministic methods are not capable to produce well-approximated models. NNDE outperforms the other approaches.
KeywordsChaotic time series Forecasting Nearest neighbor algorithm Evolutionary algorithms
José R. Cedeño’s doctoral program has been funded by CONACYT Scholarship No. 516226/290379.
Compliance with ethical standards
Conflict of interest
Authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Boné R, Crucianu M (2002) Multi-step-ahead prediction with neural networks: a review. Temes Rencontres Int Approch Connex en Sci 2:97–106Google Scholar
- De La Vega E, Flores JJ, Graff M (2014) k-nearest-neighbor by differential evolution for time series forecasting. In: Nature-inspired computation and machine learning, Springer, pp 50–60Google Scholar
- Kumar H, Patil SB (2015) Estimation & forecasting of volatility using Arima, Arfima and neural network based techniques. In: 2015 IEEE international advance computing conference (IACC), IEEE, pp 992–997Google Scholar
- Lampinen J, Zelinka I (1999) Mixed integer-discrete-continuous optimization by differential evolution. In: Proceedings of the 5th international conference on soft computing, pp 71–76Google Scholar
- R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. http://www.R-project.org (ISBN 3-900051-07-0)
- Rothman P (2012) Nonlinear time series analysis of economic and financial data. Springer, New YorkGoogle Scholar
- Schroeder D (2000) Astronomical optics. Electronics and Electrical, Academic Press. https://books.google.com.mx/books?id=v7E25646wz0C
- Sorjamaa Antti LA (2006) Time series prediction using direct strategy. In: ESANN’2006 proceedings—European symposium on artificial neural networks Bruges (Belgium), Springer, pp 143–148Google Scholar
- Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359Google Scholar
- Tao D, Hongfei X (2007) Chaotic time series prediction based on radial basis function network. In: Eighth ACIS international conference on software engineering, artificial intelligence, networking, and parallel/distributed computing, 2007. SNPD 2007, IEEE, vol 1, pp 595–599Google Scholar
- Xu LMX (2007) Rbf network-based chaotic time series prediction and its application in foreign exchange market. In: Proceedings of the international conference on intelligent systems and knowledge engineering (ISKE 2007)Google Scholar