Title: Forecasting agricultural commodities prices using deep learning-based models: basic LSTM, bi-LSTM, stacked LSTM, CNN LSTM, and convolutional LSTM

Authors: R. Murugesan; Eva Mishra; Akash Hari Krishnan

Addresses: Department of Humanities and Social Sciences, National Institute of Technology, Tiruchirappalli, Tamil Nadu, 620015, India ' Department of Humanities and Social Sciences, National Institute of Technology, Tiruchirappalli, Tamil Nadu, 620015, India ' Department of Computer Science and Engineering, Tandon School of Engineering, New York University, 10025, USA

Abstract: The literature argues that an accurate price prediction of agricultural goods is quintessential to ensure the economy's good functioning worldwide. Research reveals that studies with the application of deep learning in the tasks of agricultural price forecast on short historical agricultural price data are very scarce. The gap mentioned above is removed in this study by employing five versions of LSTM deep learning techniques for five agricultural commodities prices prediction on a univariate time series dataset of rice, wheat, gram, banana, and groundnut spanning January 2000 to July 2020. The study obtained good forecasting results for all five commodities employing the five LSTM models. The study validated the results with lower values of error metrics, MAE, MAPE, MSE, and RMSE, and two paired t-tests with hypothesis and confidence levels of 95% as a measure of robustness. The study predicted one month ahead future price and compared it with actual prices using LSTM models.

Keywords: agricultural commodities price forecast; deep learning models; basic LSTM; bi-LSTM; stacked LSTM; CNN LSTM; convolutional LSTM.

DOI: 10.1504/IJSAMI.2022.125757

International Journal of Sustainable Agricultural Management and Informatics, 2022 Vol.8 No.3, pp.242 - 277

Received: 26 Aug 2021
Accepted: 10 May 2022
Published online: 27 Sep 2022 *

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