Abstract
In this paper we introduce a fuzzy pattern operator and propose a new weighting fuzzy time series strategy for generating accurate ex-post forecasts. A decision support system is built for managing the weights of the information provided by the historical data, under a fuzzy time series framework. Our procedure analyzes the historical performance of the time series using different experiments, and it classifies the characteristics of the series through a fuzzy operator, providing a trapezoidal fuzzy number as one-step ahead forecast. We also present some numerical results related to the predictive performance of our procedure with time series of financial data sets.
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Acknowledgments
Research partially supported by the Ministerio de EconomÃa y Competitividad, España (project MTM2014-56233-P, co-financed by FEDER funds).
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Rubio, A., Vercher, E., Bermúdez, J.D. (2017). Introducing a Fuzzy-Pattern Operator in Fuzzy Time Series. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10306. Springer, Cham. https://doi.org/10.1007/978-3-319-59147-6_14
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DOI: https://doi.org/10.1007/978-3-319-59147-6_14
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