N-BEATS Deep Learning Architecture for Agricultural Commodity Price Forecasting

IF 2.3 3区 农林科学 Q1 AGRONOMY
G. H. Harish Nayak, Md Wasi Alam, G. Avinash, K. N. Singh, Mrinmoy Ray, Rajeev Ranjan Kumar
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Abstract

Agricultural commodity prices have unique characteristics and tend to fluctuate more due to seasonality, inelastic demand, and production uncertainty. Additionally, the considerable volatility observed in time series data amplifies the complexity, presenting a notable challenge. This paper addresses the intricate challenges associated with forecasting agricultural commodity prices, which are characterized by seasonality, inelastic demand, and production uncertainty. We introduce deep learning (DL) models to navigate the complexities of nonlinear and nonstationary price data in the agricultural sector. Despite the success of DL models in handling intricate data, their original design for tasks like image processing and natural language processing necessitates specialized architectures for time series forecasting. To meet this demand, we evaluate the neural basis expansion analysis for interpretable time series forecasting (N-BEATS) model, a novel architecture designed specifically for time series forecasting, on weekly potato price data collected from the Farrukhabad market in Uttar Pradesh between January 2003 and August 2023. A comparative analysis is conducted with three other models: convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent units (GRU) using the same dataset. Various forecasting evaluation criteria, including root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), are employed to assess and compare the models’ performance. Empirical results demonstrate that the N-BEATS model consistently outperforms the other models across all evaluation criteria. Furthermore, the Diebold–Mariano (DM) test confirms the significant forecasting advantage of the N-BEATS model over the other sequential models. This research showcases the potential of the N-BEATS model in enhancing the precision of agricultural commodity price forecasting, with implications for stakeholders such as farmers and planners. The findings contribute to advancing the understanding of deep learning applications in the agricultural domain, offering a promising avenue for more accurate and reliable forecasting methods.

Abstract Image

用于农产品价格预测的 N-BEATS 深度学习架构
农产品价格具有独特性,由于季节性、需求缺乏弹性和生产的不确定性,其波动往往更大。此外,在时间序列数据中观察到的相当大的波动性放大了复杂性,带来了显著的挑战。农产品价格具有季节性、需求无弹性和生产不确定性等特点,本文探讨了与预测农产品价格相关的复杂挑战。我们引入了深度学习(DL)模型,以应对农业领域非线性和非平稳价格数据的复杂性。尽管深度学习模型在处理复杂数据方面取得了成功,但由于其最初是为图像处理和自然语言处理等任务而设计的,因此有必要为时间序列预测建立专门的架构。为了满足这一需求,我们在 2003 年 1 月至 2023 年 8 月期间从北方邦 Farrukhabad 市场收集的每周马铃薯价格数据上,评估了可解释时间序列预测(N-BEATS)神经基础扩展分析模型,这是一种专为时间序列预测而设计的新型架构。使用相同的数据集,与其他三种模型进行了比较分析:卷积神经网络(CNN)、长短期记忆(LSTM)和门控递归单元(GRU)。采用了各种预测评估标准,包括均方根误差 (RMSE)、平均绝对误差 (MAE) 和平均绝对百分比误差 (MAPE),以评估和比较模型的性能。实证结果表明,在所有评估标准中,N-BEATS 模型的性能始终优于其他模型。此外,Diebold-Mariano(DM)检验证实,N-BEATS 模型的预测优势明显优于其他序列模型。这项研究展示了 N-BEATS 模型在提高农产品价格预测精度方面的潜力,对农民和规划者等利益相关者具有重要意义。研究结果有助于推进对深度学习在农业领域应用的理解,为更准确、更可靠的预测方法提供了一条大有可为的途径。
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来源期刊
Potato Research
Potato Research AGRONOMY-
CiteScore
5.50
自引率
6.90%
发文量
66
审稿时长
>12 weeks
期刊介绍: Potato Research, the journal of the European Association for Potato Research (EAPR), promotes the exchange of information on all aspects of this fast-evolving global industry. It offers the latest developments in innovative research to scientists active in potato research. The journal includes authoritative coverage of new scientific developments, publishing original research and review papers on such topics as: Molecular sciences; Breeding; Physiology; Pathology; Nematology; Virology; Agronomy; Engineering and Utilization.
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