Memory based neural network for cumin price forecasting in Gujarat, India

Abstract

Agricultural price forecasting, with its distinctive characteristics, remains a captivating field of study. In countries like India, grappling with food security challenges, reliable and efficient price forecasting models are of utmost importance. This research focuses on accurate prediction of cumin prices by emphasizing the importance of time series forecasting and the adoption of deep learning models to overcome the limitations of traditional statistical approaches. Deep learning (DL) approaches including Deep Neural Network (DNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) were employed to forecast cumin prices for the entire year (365 days) of 2022. The models were trained and assessed using daily price data from 2002 to 2021 from Unjha market, Gujarat, India, with accuracy metrics including RMSE, MAPE, SMAPE, MASE, and MDA. The superior accuracy of the Stacked LSTM model, particularly its low RMSE, MAPE, and SMAPE scores, along with the highest MDA, marks it as a promising tool for future agricultural price forecasting. Its precision in predicting cumin prices, with a 5 % error pre-sowing and 18 % pre-harvesting, is particularly noteworthy during critical farming periods. These findings can guide farmers in aligning their production schedules with periods of high prices, aiding in economically more beneficial farming practices. Additionally, the model's predictive reliability can assist policymakers and traders in making data-driven decisions, thus playing a significant role in stabilizing market dynamics.

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