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Comparative performance of wavelet-based neural network approaches

  • Priyanka Anjoy
  • Ranjit Kumar PaulEmail author
Original Article
  • 241 Downloads

Abstract

An agriculture-dominated developing country like India has been always in need of efficient and reliable time series forecasting methodologies to describe various agricultural phenomenons, whereas agricultural price forecasting continue to be the challenging areas in this domain. The observed features of many temporal price data set constitute complex nonlinearity, and modeling these features often go beyond the capability of Box–Jenkins autoregressive integrated moving average methodology. Moreover, despite the popularity and sheer power of traditional neural network model, the empirical forecasting performance of this model has not been found satisfactory in all cases. To address the problem, wavelet-based modeling approach is recently upsurging. Present study discusses two wavelet-based neural network approaches envisaging monthly wholesale onion price of three markets, namely Bangalore, Hubli, and Solapur. Wavelet-based decomposition makes it possible to describe the useful pattern of the series from both global as well as local aspects and found to be highly proficient in denoising and capturing the inherent pattern of the series through a distinctive approach. Besides, wavelet method can also be used as a tool for function approximation. The improvement upon time-delay neural network also be made up to a great extent through using wavelet-based approaches as exhibited through proper empirical evidence.

Keywords

ARIMA MODWT Nonlinearity TDNN Wavelet 

Notes

Acknowledgements

We would like to express our sincere thanks and gratitude to the anonymous reviewers for their valuable suggestions that helped us a lot in improving this manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

References

  1. 1.
    Anjoy P, Paul RK (2017) Wavelet based hybrid approach for forecasting volatile potato price. J Indian Soc Agric Stat 71(1):7–14MathSciNetGoogle Scholar
  2. 2.
    Anjoy P, Paul RK, Sinha K, Paul AK, Ray M (2017) A hybrid wavelet based neural networks model for predicting monthly wpi of pulses in India. Indian J Agric Sci 87(6):834–839Google Scholar
  3. 3.
    Adhikari R, Agrawal RK (2014) A combination of artificial neural network and random walk models for financial time series forecasting. Neural Comput Appl 24(6):1441–1449CrossRefGoogle Scholar
  4. 4.
    Alexandridis AK, Zapranis AD (2013) Wavelet neural networks: a practical guide. Neural Netw 42:1–27CrossRefzbMATHGoogle Scholar
  5. 5.
    Antoniadis A (1997) Wavelets in statistics: a review. J Ita Stat Soc 6:97–144CrossRefGoogle Scholar
  6. 6.
    Díaz-Robles LA, Ortega JC, Fu JS, Reed GD, Chow JC, Watson JG, Moncada-Herrera JA (2008) A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: the case of Temuco, Chile. Atmos Environ 42:8331–8340CrossRefGoogle Scholar
  7. 7.
    Farda AK, Akbari-Zadehb MR (2014) A hybrid method based on wavelet, ANN and ARIMA model for short-term load forecasting. J Exp Theor Artif Intell 26(2):167–182CrossRefGoogle Scholar
  8. 8.
    Granger CWJ, Anderson AP (1978) Introduction to bilinear time series models. Vandenhoeck and Ruprecht, GottingenGoogle Scholar
  9. 9.
    Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, Englewood CliffszbMATHGoogle Scholar
  10. 10.
    Hung NQ, Babel MS, Weesakul S, Tripathi NK (2009) An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrol Earth Syst Sci 13:1413–1425CrossRefGoogle Scholar
  11. 11.
    Kohzadi N, Boyd MS, Kermanshahi B, Kaastra I (1995) A comparison of artificial neural network and time series models for forecasting commodity prices. Neurocomputing 10:169–181CrossRefzbMATHGoogle Scholar
  12. 12.
    Kuan CM, White H (1994) Artificial neural networks: an econometric perspective. Econ Rev 13:1–91MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    McLeod AI, Li WK (1983) Diagnostic checking ARMA time series models using squared residual autocorrelations. J Time Ser Anal 4:269–273MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Mohammadi K, Eslami HR, Dardashti D (2005) Comparison of regression, ARIMA and ANN models for reservoir inflow forecasting using snowmelt equivalent (a case study of Karaj). J Agric Sci Technol 7:17–30Google Scholar
  15. 15.
    Pacelli V, Bevilacqua V, Azzollini M (2011) An artificial neural network model to forecast exchange rates. J Intell Learn Syst Appl 3:57–69Google Scholar
  16. 16.
    Paul RK, Prajneshu GH (2013) Statistical modelling for forecasting of wheat yield based on weather variables. Indian J Agric Sci 83(2):180–183Google Scholar
  17. 17.
    Paul RK, Das MK (2013) Forecasting of average annual fish landing in Ganga Basin. Fish Chimes 33(3):51–54Google Scholar
  18. 18.
    Paul RK, Prajneshu GH (2013) Wavelet frequency domain approach for modelling and forecasting of Indian monsoon rainfall time-series data. J Indian Soc Agric Stat 67(3):319–327MathSciNetGoogle Scholar
  19. 19.
    Paul RK, Alam W, Paul AK (2014) Prospects of livestock and dairy production in India under time series framework. Indian J Anim Sci 84(4):130–134Google Scholar
  20. 20.
    Paul RK (2015) ARIMAX-GARCH-WAVELET model for forecasting volatile data. Model Assist Stat Appl 10(3):243–252MathSciNetGoogle Scholar
  21. 21.
    Paul RK, Gurung B, Paul AK (2015) Modelling and forecasting of retail price of arhar dal in Karnal, Haryana. Indian J Agric Sci 85(1):69–72Google Scholar
  22. 22.
    Paul RK, Sinha K (2016) Forecasting crop yield: a comparative assessment of ARIMAX and NARX model. RASHI 1(1):77–85Google Scholar
  23. 23.
    Percival DB, Walden AT (2000) Wavelet methods for time series analysis. Cambridge University Press, CambridgeCrossRefzbMATHGoogle Scholar
  24. 24.
    Tong H, Lim KS (1980) Threshold autoregressive, limit cycles and cyclical data. J R Stat Soc Ser B Methodol 42:245–292zbMATHGoogle Scholar
  25. 25.
    Vidakovic B (1999) Statistical modeling by wavelets. Wiley, New YorkCrossRefzbMATHGoogle Scholar
  26. 26.
    Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175CrossRefzbMATHGoogle Scholar
  27. 27.
    Zhang GP, Qi M (2005) Neural network forecasting for seasonal and trend time series. Eur J Oper Res 160(2):501–514MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.ICAR-Indian Agricultural Statistics Research InstituteNew DelhiIndia

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