Agricultural commodity price prediction using long short-term memory (LSTM) based neural networks Jaiswal Ronit1,2, Jha Girish K.1,*, Choudhary Kapil1, Ranjan Rajeev Kumar3 1ICAR-Indian Agricultural Research Institute, New Delhi-110 012, India 2ICAR-Central Institute of Temperate Horticulture, Srinagar-191 132, Jammu and Kashmir, India 3ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110 012, India *Corresponding Author: Girish K. Jha, ICAR-Indian Agricultural Research Institute, New Delhi-110 012, India, Email: girish.stat@gmail.com
Online published on 21 November, 2023. Abstract Background Agricultural price forecasting is one of the research hotspots in time series forecasting due to its unique characteristics. In this paper, we develop a standard long short-term memory (LSTM) for accurately predicting a nonstationary and nonlinear agricultural price series. Methods An LSTM model effectively analyses and captures short-term and long-term temporal patterns of a complex time series due to its recurrent neural architecture and the memory function used in the hidden nodes. Result The empirical results using the international monthly price series of maize demonstrate the superiority of the developed LSTM model over other models in terms of various forecasting evaluation criteria. Overall, LSTM model shows great potential for improving the accuracy and reliability of agricultural price predictions, benefiting farmers, traders and policymakers in making informed decisions. Top Keywords Long short-term memory, Price forecasting, Time-delay neural networks. Top |