Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Boné R, Crucianu M (2002) Multi-step-ahead prediction with neural networks: a review. Temes Rencontres Int Approch Connex en Sci 2:97–106
Box GE, Jenkins GM, Reinsel GC (2011) Time series analysis: forecasting and control. Wiley, New York
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 50–60
Farmer JD, Sidorowich JJ (1987) Predicting chaotic time series. Phys Rev Lett 59(8):845
Hénon M (1976) A two-dimensional mapping with a strange attractor. Commun Math Phys 50:69–77
Kantz H, Schreiber T (2004) Nonlinear time series analysis, 2nd edn. Cambridge University Press, Cambridge
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 992–997
Lampinen J, Zelinka I (1999) Mixed integer-discrete-continuous optimization by differential evolution. In: Proceedings of the 5th international conference on soft computing, pp 71–76
Leung H, Lo T, Wang S (2001) Prediction of noisy chaotic time series using an optimal radial basis function neural network. IEEE Trans Neural Netw 12(5):1163–1172
Lorenz EN (1963) Deterministic nonperiodic flow. J Atmos Sci 20:130–148
Mackey MC, Glass L (1977) Oscillation and chaos in physiological control systems. Science 197(4300):287–289
Matías JM, Reboredo JC (2012) Forecasting performance of nonlinear models for intraday stock returns. J Forecast 31(2):172–188. doi:10.1002/for.1218
Montgomery DC, Jennings CL, Kulahci M (2016) Introduction to time series analysis and forecasting. Wiley, Hoboken
Olmedo E (2016) Comparison of near neighbour and neural network in travel forecasting. J Forecast 35(3):217–223. doi:10.1002/for.2370
Palit AK, Popovic D (2005) Computational intelligence in time series forecasting. Springer, New York
Price K, Storn RM, Lampinen JA (2006) Differential evolution: a practical approach to global optimization. Springer, New York
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)
Rossler O (1979) An equation for hyperchaos. Phys Lett A 71(2):155–157
Rothman P (2012) Nonlinear time series analysis of economic and financial data. Springer, New York
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 143–148
Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359
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 595–599
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)
Zhang JS, Xiao XC (2000) Predicting chaotic time series using recurrent neural network. Chin Phys Lett 17(2):88
Acknowledgements
José R. Cedeño’s doctoral program has been funded by CONACYT Scholarship No. 516226/290379.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Authors declare that they have no conflict of interest.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Communicated by V. Loia.
Rights and permissions
About this article
Cite this article
Flores, J.J., González, J.R.C., Farias, R.L. et al. Evolving nearest neighbor time series forecasters. Soft Comput 23, 1039–1048 (2019). https://doi.org/10.1007/s00500-017-2822-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-017-2822-1