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A New Bootstrapped Hybrid Artificial Neural Network Approach for Time Series Forecasting

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  • Erol Eğrioğlu

    (Giresun University
    Lancaster University)

  • Robert Fildes

    (Lancaster University)

Abstract

In this study, a new bootstrapped hybrid artificial neural network is proposed for forecasting. This new neural network provides input significance, linearity and nonlinearity hypothesis tests in a unique network structure via a residual bootstrap approach. The network has three parts: linear, non-linear and a combination with associated weights and biases. These weights are used to test the input significance, linearity and nonlinearity hypotheses with this new method providing empirical distributions for forecasts and weights. The proposed method employs a bagging approach to obtain forecasts. It is then applied to real-time series including the M4 Competition data set and stock exchange time series where its performance is compared with appropriate benchmark methods including other popular neural networks. The proposed method results are less affected than other neural networks by initial random weights, which means that the results of the proposed method are more stable and precise. The new method provides improvements in forecasting accuracy over the established benchmarks.

Suggested Citation

  • Erol Eğrioğlu & Robert Fildes, 2022. "A New Bootstrapped Hybrid Artificial Neural Network Approach for Time Series Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1355-1383, April.
    • Handle: RePEc:kap:compec:v:59:y:2022:i:4:d:10.1007_s10614-020-10073-7
    DOI: 10.1007/s10614-020-10073-7
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    1. McMillan, David G., 2007. "Non-linear forecasting of stock returns: Does volume help?," International Journal of Forecasting, Elsevier, vol. 23(1), pages 115-126.
    2. Hyndman, Rob J., 2020. "A brief history of forecasting competitions," International Journal of Forecasting, Elsevier, vol. 36(1), pages 7-14.
    3. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660, July.
    4. Timmermann, Allan & Granger, Clive W. J., 2004. "Efficient market hypothesis and forecasting," International Journal of Forecasting, Elsevier, vol. 20(1), pages 15-27.
    5. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2018. "The M4 Competition: Results, findings, conclusion and way forward," International Journal of Forecasting, Elsevier, vol. 34(4), pages 802-808.
    6. Dantas, Tiago Mendes & Cyrino Oliveira, Fernando Luiz, 2018. "Improving time series forecasting: An approach combining bootstrap aggregation, clusters and exponential smoothing," International Journal of Forecasting, Elsevier, vol. 34(4), pages 748-761.
    7. Ebrahimpour, Reza & Nikoo, Hossein & Masoudnia, Saeed & Yousefi, Mohammad Reza & Ghaemi, Mohammad Sajjad, 2011. "Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange," International Journal of Forecasting, Elsevier, vol. 27(3), pages 804-816.
    8. Sarantis, Nicholas, 2001. "Nonlinearities, cyclical behaviour and predictability in stock markets: international evidence," International Journal of Forecasting, Elsevier, vol. 17(3), pages 459-482.
    9. Gorr, Wilpen L. & Nagin, Daniel & Szczypula, Janusz, 1994. "Comparative study of artificial neural network and statistical models for predicting student grade point averages," International Journal of Forecasting, Elsevier, vol. 10(1), pages 17-34, June.
    10. Balkin, Sandy D. & Ord, J. Keith, 2000. "Automatic neural network modeling for univariate time series," International Journal of Forecasting, Elsevier, vol. 16(4), pages 509-515.
    11. Masarotto, Guido, 1990. "Bootstrap prediction intervals for autoregressions," International Journal of Forecasting, Elsevier, vol. 6(2), pages 229-239, July.
    12. Lohrmann, Christoph & Luukka, Pasi, 2019. "Classification of intraday S&P500 returns with a Random Forest," International Journal of Forecasting, Elsevier, vol. 35(1), pages 390-407.
    13. Lam, J. -P. & Veall, M. R., 2002. "Bootstrap prediction intervals for single period regression forecasts," International Journal of Forecasting, Elsevier, vol. 18(1), pages 125-130.
    14. Granger, Clive W. J., 1992. "Forecasting stock market prices: Lessons for forecasters," International Journal of Forecasting, Elsevier, vol. 8(1), pages 3-13, June.
    15. Barrow, Devon K. & Crone, Sven F., 2016. "Cross-validation aggregation for combining autoregressive neural network forecasts," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1120-1137.
    16. Bradley, Michael D. & Jansen, Dennis W., 2004. "Forecasting with a nonlinear dynamic model of stock returns and industrial production," International Journal of Forecasting, Elsevier, vol. 20(2), pages 321-342.
    17. Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
    18. Nyberg, Henri, 2011. "Forecasting the direction of the US stock market with dynamic binary probit models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 561-578.
    19. Nyberg, Henri, 2011. "Forecasting the direction of the US stock market with dynamic binary probit models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 561-578, April.
    20. Olson, Dennis & Mossman, Charles, 2003. "Neural network forecasts of Canadian stock returns using accounting ratios," International Journal of Forecasting, Elsevier, vol. 19(3), pages 453-465.
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