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

Sandip Garai, Ranjit Kumar Paul (), Debopam Rakshit, Md Yeasin (), Walid Emam, Yusra Tashkandy and Christophe Chesneau
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Sandip Garai: ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, 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

Mathematics, 2023, vol. 11, issue 13, 1-18

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.

Keywords: decomposition; interrelations; machine learning; nonlinearity; wavelet filters (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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