Research on Long Sequence Time Series Forecasting About Price Trend of Bulk Agricultural Commodity

Jinquan Kang, Gang Zhao () and Zechen Wang
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Jinquan Kang: Beijing Information Science and Technology University
Gang Zhao: Beijing Information Science and Technology University
Zechen Wang: Beijing Information Science and Technology University

A chapter in LISS 2024, 2025, pp 806-817 from Springer

Abstract: Abstract As important components of production raw materials and daily necessities, predicting the future long-term price trends of agricultural products is of great significance. The prices of bulk agricultural commodity usually fluctuate widely and have complex relationships with multiple factors, which makes long-term price forecasting difficult. We propose a long sequence time-series forecasting method based on the Multi-decomposition Attentional Gate Recurrent Unit (MAGRU) model, addressing two issues of existing bulk commodity price forecasting methods: weak response to price volatility and inadequate utilization of other covariates related to prices. The cotton price index is used as an example to study and analyze long sequence time-series forecasting algorithms. We select six covariates from three perspectives to improve the quality of prediction. Experimental results show that our model achieved an average decrease of 49.6%, 60.4%, 51.2%, and 36.8% in MSE compared to LSTM, TCN, GRU, and DLinear, respectively. This effectively reduces errors in predicting long-term prices of bulk commodities.

Keywords: Gated Recurrent Unit Network; Attention mechanism; bulk agricultural commodity; price forecasting; Long Sequence Time-series Forecasting (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-96-9697-0_61

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DOI: 10.1007/978-981-96-9697-0_61

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