Tomato price forecasting using a stacked LSTM network with global scaling CNN and RC layer
G Sumaiya Farzana1 and N Prakash2
Department of Computer Science and Engineering,B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, Tamil Nadu,India1
Department of Computer Science,B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, Tamil Nadu,India2
Department of Computer Science,B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, Tamil Nadu,India2
Corresponding Author : G Sumaiya Farzana
Recieved : 15-March-2024; Revised : 12-April-2025; Accepted : 14-April-2025
Abstract
Accurate price prediction is essential across various sectors such as real estate, e-commerce, and finance. Traditional methods often struggle with high-dimensional data and nonlinear patterns. Recent advancements in machine learning (ML), particularly deep learning (DL) techniques such as convolutional neural networks (CNN) and collaborative models like StackNet, offer promising solutions. The proposed approach leverages advanced DL techniques, specifically the global scaling convolutional neural network (GS-CNN) and the StackNet rapid curing (RC) Layer. In this framework, long short-term memory (LSTM) models are organized in a stacked configuration to reduce kernel size and enable effective information flow between layers, thereby achieving optimal predictions with lower error margins for tomato price forecasting. The performance of the proposed model is evaluated using standard error metrics, including mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²). The experimental results indicate that the model achieves an MSE of 0.0046, RMSE of 0.06, MAPE of 0.2753, MAE of 0.05, and an R² value of 0.83. These outcomes confirm the effectiveness and adaptability of the proposed approach in accurately forecasting tomato prices with minimal error rates. However, limited knowledge of underlying market dynamics may still constrain prediction accuracy. While the proposed model demonstrates low error rates, further improvement is possible by incorporating larger and more diverse datasets to enhance predictive performance.
Keywords
Price prediction, Deep learning, StackNet-RC, Global scaling CNN, Tomato forecasting, Stacked LSTM.
Cite this article
Farzana GS, Prakash N. Tomato price forecasting using a stacked LSTM network with global scaling CNN and RC layer. International Journal of Advanced Technology and Engineering Exploration. 2025;12(125):653-669. DOI : 10.19101/IJATEE.2024.111100298
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