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Forecasting in Fuzzy Time Series by an Extension of Simple Exponential Smoothing

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Advances in Artificial Intelligence -- IBERAMIA 2014 (IBERAMIA 2014)

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Abstract

Time Series was introduced to improve the forecasting made by statistical methods in vague or imprecise data and in time series with few samples available. However, the integration of these concepts is a little explored area. In this paper we introduced a new forecast model composed by a pre-processing method and a predicting method. The pre-processing method is responsible for analyzing the data and defining a suitable structure of representation. The predicting method is based on the combination of fuzzy time series concepts with the simple exponential smoothing, a traditional statistical method for prediction. The experiments performed with the TAIEX index show that, besides obtaining better accuracy rates when compared with other methods available in the literature, the predictions made over the whole time series had the same behavior and trends than the real data.

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Author information

Authors and Affiliations

  1. Federal University of São Carlos, São Carlos, Brazil

    Fábio José Justo dos Santos & Heloisa de Arruda Camargo

  2. Federal Institute of Education, Science and Technology of São Paulo, São Paulo, Brazil

    Fábio José Justo dos Santos

Authors
  1. Fábio José Justo dos Santos
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  2. Heloisa de Arruda Camargo
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Corresponding author

Correspondence to Fábio José Justo dos Santos .

Editor information

Editors and Affiliations

  1. Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

    Ana L.C. Bazzan

  2. Pontifica Universidad Católica (PUC), Santiago de Chile, Chile

    Karim Pichara

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dos Santos, F.J.J., de Arruda Camargo, H. (2014). Forecasting in Fuzzy Time Series by an Extension of Simple Exponential Smoothing. In: Bazzan, A., Pichara, K. (eds) Advances in Artificial Intelligence -- IBERAMIA 2014. IBERAMIA 2014. Lecture Notes in Computer Science(), vol 8864. Springer, Cham. https://doi.org/10.1007/978-3-319-12027-0_21

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  • DOI: https://doi.org/10.1007/978-3-319-12027-0_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12026-3

  • Online ISBN: 978-3-319-12027-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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