IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i13p2896-d1181520.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/13/2896/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/13/2896/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    2. Rakshit, Debopam & Paul, Ranjit Kumar & Panwar, Sanjeev, 2021. "Asymmetric Price Volatility of Onion in India," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 0(Number 2), June.
    3. Ian T. Jolliffe, 1982. "A Note on the Use of Principal Components in Regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(3), pages 300-303, November.
    4. 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.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Sepehr Ramyar & Farhad Kianfar, 2019. "Forecasting Crude Oil Prices: A Comparison Between Artificial Neural Networks and Vector Autoregressive Models," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 743-761, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mehmet Kayakuş & Mustafa Terzioğlu & Dilşad Erdoğan & Selin Aygen Zetter & Onder Kabas & Georgiana Moiceanu, 2023. "European Union 2030 Carbon Emission Target: The Case of Turkey," Sustainability, MDPI, vol. 15(17), pages 1-23, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Eleni Constantinou & Robert Georgiades & Avo Kazandjian & George Kouretas, 2005. "Mean and variance causality between the Cyprus Stock Exchange and major equity markets," Working Papers 0501, University of Crete, Department of Economics.
    3. Assad L. Baunto & Christian Bordes & Samuel Maveyraud & Philippe Rous, 2007. "Money and uncertainty in the Philippines: A Friedmanite Perspective," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00308663, HAL.
    4. Mohamed Chikhi & Anne Péguin-Feissolle & Michel Terraza, 2013. "SEMIFARMA-HYGARCH Modeling of Dow Jones Return Persistence," Computational Economics, Springer;Society for Computational Economics, vol. 41(2), pages 249-265, February.
    5. Mohammadi, M. & Rezakhah, S. & Modarresi, N., 2020. "Semi-Lévy driven continuous-time GARCH process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    6. Alizadeh, Amir H. & Tamvakis, Michael, 2016. "Market conditions, trader types and price–volume relation in energy futures markets," Energy Economics, Elsevier, vol. 56(C), pages 134-149.
    7. Saghaian, Sayed & Nemati, Mehdi & Walters, Cory & Chen, Bo, 2018. "Asymmetric Price Volatility Transmission between U.S. Biofuel, Corn, and Oil Markets," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 43(01), January.
    8. Carlos Alberto Piscarreta Pinto Ferreira, 2022. "Revisiting The Determinants Of Sovereign Bond Yield Volatility," Working Papers REM 2022/0241, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    9. Charfeddine, Lanouar & Ajmi, Ahdi Noomen, 2013. "The Tunisian stock market index volatility: Long memory vs. switching regime," Emerging Markets Review, Elsevier, vol. 16(C), pages 170-182.
    10. Shi Chen & Cathy Yi-Hsuan Chen & Wolfgang Karl Hardle, 2020. "A first econometric analysis of the CRIX family," Papers 2009.12129, arXiv.org.
    11. Shafiqah Azman & Dharini Pathmanathan & Aerambamoorthy Thavaneswaran, 2022. "Forecasting the Volatility of Cryptocurrencies in the Presence of COVID-19 with the State Space Model and Kalman Filter," Mathematics, MDPI, vol. 10(17), pages 1-15, September.
    12. Cheung, Yin-Wong & Chung, Sang-Kuck, 2009. "A Long Memory Model with Mixed Normal GARCH for US Inflation Data," Santa Cruz Department of Economics, Working Paper Series qt2202s99q, Department of Economics, UC Santa Cruz.
    13. da Silva, Carlos Alberto & Ferreira, Leo da, 2015. "Asymmetric Volatility Modeling of Spot Prices of Arabic Coffee in Brazil," 2015 Conference, August 9-14, 2015, Milan, Italy 211556, International Association of Agricultural Economists.
    14. Cong, Ren & Lo, Alex Y., 2017. "Emission trading and carbon market performance in Shenzhen, China," Applied Energy, Elsevier, vol. 193(C), pages 414-425.
    15. Liu, Feng & Zhang, Chuanguo & Tang, Mengying, 2021. "The impacts of oil price shocks and jumps on China's nonferrous metal markets," Resources Policy, Elsevier, vol. 73(C).
    16. Pami Dua & Nishita Raje & Satyananda Sahoo, 2004. "Interest Rate Modeling and Forecasting in India," Occasional papers 3, Centre for Development Economics, Delhi School of Economics.
    17. Mushtaq Hussain Khan & Junaid Ahmed & Mazhar Mughal & Imtiaz Hussain Khan, 2023. "Oil price volatility and stock returns: Evidence from three oil‐price wars," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 3162-3182, July.
    18. Myers, Robert J., 1994. "Time Series Econometrics and Commodity Price Analysis: A Review," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 62(02), pages 1-15, August.
    19. Chia-Lin Chang & Michael McAleer & Guangdong Zuo, 2017. "Volatility Spillovers and Causality of Carbon Emissions, Oil and Coal Spot and Futures for the EU and USA," Sustainability, MDPI, vol. 9(10), pages 1-22, October.
    20. Korkmaz, Turhan & Cevik, Emrah Ismail & Gurkan, Serhan, 2010. "Testing the international capital asset pricing model with Markov switching model in emerging markets," MPRA Paper 71481, University Library of Munich, Germany, revised 2010.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2896-:d:1181520. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.