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Median-Pi artificial neural network for forecasting

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Abstract

Datasets with outliers can be predicted with robust learning methods or robust artificial neural networks. In robust artificial neural networks, the architectures become robust by using robust statistics as aggregation functions. Median neural network and trimmed mean neural network are two robust artificial neural networks used in the literature. In these robust artificial neural networks, median and trimmed mean statistics are used as aggregation functions. In this study, Median-Pi artificial neural network is proposed as a new robust neural network for the purpose of forecasting. In Median-Pi artificial neural network, median and multiplicative functions are used as aggregation functions. Because of using median, the proposed network can produce good results for data with outliers. The Median-Pi artificial neural network is trained by particle swarm optimization. The performance of the neural network is investigated by using datasets from the International Time Series Forecast Competition 2016 (CIF-2016). The performance of the proposed method in case of outlier is compared to some other artificial neural networks. Median neural network, trimmed mean neural network, Pi-Sigma neural network and the proposed robust network are applied to time series with outlier, and the obtained results are compared. According to application results, the proposed Median-Pi artificial neural network can produce better forecast results than the other network types.

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Authors and Affiliations

  1. Department of Statistics, Forecast Research Laboratory, Giresun University, 28000, Giresun, Turkey

    Erol Egrioglu, Eren Bas & Ali Zafer Dalar

  2. Department of Econometrics, Forecast Research Laboratory, Giresun University, 28000, Giresun, Turkey

    Ufuk Yolcu

Authors
  1. Erol Egrioglu
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  2. Ufuk Yolcu
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  3. Eren Bas
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  4. Ali Zafer Dalar
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Corresponding author

Correspondence to Ufuk Yolcu.

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Egrioglu, E., Yolcu, U., Bas, E. et al. Median-Pi artificial neural network for forecasting. Neural Comput & Applic 31, 307–316 (2019). https://doi.org/10.1007/s00521-017-3002-z

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  • DOI: https://doi.org/10.1007/s00521-017-3002-z

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