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.
CITATION STYLE
Rubio, A., Vercher, E., & Bermúdez, J. D. (2017). Introducing a fuzzy-pattern operator in fuzzy time series. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10306 LNCS, pp. 154–164). Springer Verlag. https://doi.org/10.1007/978-3-319-59147-6_14
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