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A novel wavelet artificial neural networks method to predict non-stationary time series

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  • Mitra Ghanbarzadeh
  • Mina Aminghafari

Abstract

There are two main ways to predict a time series: linear and non-linear. But none of them alone is suitable for prediction of data with both linear and non-linear patterns. One of the popular non-linear methods is an artificial neural network. We present a new hybrid prediction method using the artificial neural networks and the wavelet stepwise regression (as a linear method). This method first predicts a time series by the wavelet stepwise regression and then predicts the residuals of this part by the artificial neural network. The performance of the new method is evaluated on simulated and real data sets. The results display that the proposed method predicts the time series better than artificial neural network, wavelet stepwise regression and three other methods.

Suggested Citation

  • Mitra Ghanbarzadeh & Mina Aminghafari, 2020. "A novel wavelet artificial neural networks method to predict non-stationary time series," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(4), pages 864-878, February.
    • Handle: RePEc:taf:lstaxx:v:49:y:2020:i:4:p:864-878
    DOI: 10.1080/03610926.2018.1549259
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    Cited by:

    1. Huazhu Xue & Hui Wu & Guotao Dong & Jianjun Gao, 2023. "A Hybrid Forecasting Model to Simulate the Runoff of the Upper Heihe River," Sustainability, MDPI, vol. 15(10), pages 1-19, May.

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