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Agricultural commodity futures prices prediction via long- and short-term time series network

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  • Hongbing Ouyang
  • Xiaolu Wei
  • Qiufeng Wu

Abstract

In this study, we attempt to predict global agricultural commodity futures prices through analysis of multivariate time series. Our motivation is based on the notion that datasets of agricultural commodity futures prices involves a mixture of long- and short-term information, linear and non-linear structure, for which traditional approaches such as Auto-Regressive Integrated Moving Average (ARIMA) and Vector Auto-Regression (VAR) may fail. To tackle this issue, Long- and Short-Term Time-series Network (LSTNet) is applied for prediction. Empirical results show that LSTNet achieves better performance over that of several state-of-the-art baseline methods on average and in most periods based on three performance evaluation measures and two tests of performance difference.

Suggested Citation

  • Hongbing Ouyang & Xiaolu Wei & Qiufeng Wu, 2019. "Agricultural commodity futures prices prediction via long- and short-term time series network," Journal of Applied Economics, Taylor & Francis Journals, vol. 22(1), pages 468-483, January.
    • Handle: RePEc:taf:recsxx:v:22:y:2019:i:1:p:468-483
    DOI: 10.1080/15140326.2019.1668664
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    Cited by:

    1. Gustavo Carvalho Santos & Flavio Barboza & Antônio Cláudio Paschoarelli Veiga & Mateus Ferreira Silva, 2021. "Forecasting Brazilian Ethanol Spot Prices Using LSTM," Energies, MDPI, vol. 14(23), pages 1-15, November.
    2. Marek Vochozka & Svatopluk Janek & Zuzana Rowland, 2023. "Coffee as an Identifier of Inflation in Selected US Agglomerations," Forecasting, MDPI, vol. 5(1), pages 1-17, January.
    3. Anna Szczepańska-Przekota, 2022. "Causality in Relation to Futures and Cash Prices in the Wheat Market," Agriculture, MDPI, vol. 12(6), pages 1-10, June.
    4. Ayush Jain & Smit Marvaniya & Shantanu Godbole & Vitobha Munigala, 2020. "A Framework for Crop Price Forecasting in Emerging Economies by Analyzing the Quality of Time-series Data," Papers 2009.04171, arXiv.org.
    5. Andree,Bo Pieter Johannes, 2021. "Estimating Food Price Inflation from Partial Surveys," Policy Research Working Paper Series 9886, The World Bank.
    6. Muthumanickam Dhanaraju & Poongodi Chenniappan & Kumaraperumal Ramalingam & Sellaperumal Pazhanivelan & Ragunath Kaliaperumal, 2022. "Smart Farming: Internet of Things (IoT)-Based Sustainable Agriculture," Agriculture, MDPI, vol. 12(10), pages 1-26, October.
    7. Elham Rahmani & Mohammad Khatami & Emma Stephens, 2024. "Using Probabilistic Machine Learning Methods to Improve Beef Cattle Price Modeling and Promote Beef Production Efficiency and Sustainability in Canada," Sustainability, MDPI, vol. 16(5), pages 1-19, February.

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