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Wavelets in Combination with Stochastic and Machine Learning Models to Predict Agricultural Prices

Author

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  • Sandip Garai

    (ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India
    Present Address: ICAR-Indian Institute of Agricultural Biotechnology, Ranchi 834003, India.)

  • Ranjit Kumar Paul

    (ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India)

  • Debopam Rakshit

    (ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India)

  • Md Yeasin

    (ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India)

  • Walid Emam

    (Department of Statistics and Operations Research, Faculty of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia)

  • Yusra Tashkandy

    (Department of Statistics and Operations Research, Faculty of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia)

  • Christophe Chesneau

    (Department of Mathematics, University of Caen-Normandie, 14000 Caen, France)

Abstract

Wavelet decomposition in signal processing has been widely used in the literature. The popularity of machine learning (ML) algorithms is increasing day by day in agriculture, from irrigation scheduling and yield prediction to price prediction. It is quite interesting to study wavelet-based stochastic and ML models to appropriately choose the most suitable wavelet filters to predict agricultural commodity prices. In the present study, some popular wavelet filters, such as Haar, Daubechies (D4), Coiflet (C6), best localized (BL14), and least asymmetric (LA8), were considered. Daily wholesale price data of onions from three major Indian markets, namely Bengaluru, Delhi, and Lasalgaon, were used to illustrate the potential of different wavelet filters. The performance of wavelet-based models was compared with that of benchmark models. It was observed that, in general, the wavelet-based combination models outperformed other models. Moreover, wavelet decomposition with the Haar filter followed by application of the random forest (RF) model gave better prediction accuracy than other combinations as well as other individual models.

Suggested Citation

  • Sandip Garai & Ranjit Kumar Paul & Debopam Rakshit & Md Yeasin & Walid Emam & Yusra Tashkandy & Christophe Chesneau, 2023. "Wavelets in Combination with Stochastic and Machine Learning Models to Predict Agricultural Prices," Mathematics, MDPI, vol. 11(13), pages 1-18, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2896-:d:1181520
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    References listed on IDEAS

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    5. Debopam Rakshit & Ranjit Kumar Paul & Md Yeasin & Walid Emam & Yusra Tashkandy & Christophe Chesneau, 2023. "Modeling Asymmetric Volatility: A News Impact Curve Approach," Mathematics, MDPI, vol. 11(13), pages 1-14, June.
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