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Improving the performance of fuzzy rules-based forecasters through application of FCM algorithm

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Abstract

Prediction models based on artificial intelligence techniques have been widely used in Time Series Forecasting in several areas. They are often fuzzy models or neural networks. This paper describes the development of neural and fuzzy models for forecasting time series of practical examples, and shows the comparisons of results between models, including the results of statistical modeling. The use of data clustering algorithms like Fuzzy C-Means is considered in fuzzy models.

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Authors and Affiliations

  1. Research Group on Systems and Computer Engineering, Federal University of Itajubá, Av. BPS 1303, Itajubá, MG, 37500-903, Brazil

    Claudio Paulo Faustino, Carlos Alberto M. Pinheiro & Otávio A. Carpinteiro

  2. Division of Astrophysics, National Institute of Spacial Research (INPE), Av. dos Astronautas 1758, São José dos Campos, SP, 12227-010, Brazil

    Camila Paiva Novaes

Authors
  1. Claudio Paulo Faustino
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  2. Camila Paiva Novaes
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  3. Carlos Alberto M. Pinheiro
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  4. Otávio A. Carpinteiro
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Correspondence to Claudio Paulo Faustino.

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Faustino, C.P., Novaes, C.P., Pinheiro, C.A.M. et al. Improving the performance of fuzzy rules-based forecasters through application of FCM algorithm. Artif Intell Rev 41, 287–300 (2014). https://doi.org/10.1007/s10462-011-9308-9

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  • DOI: https://doi.org/10.1007/s10462-011-9308-9

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