Memory based neural network for cumin price forecasting in Gujarat, India
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 cumi...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Journal of Agriculture and Food Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666154324000577 |
| _version_ | 1827324622767390720 |
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| author | N. Harshith Prity Kumari |
| author_facet | N. Harshith Prity Kumari |
| author_sort | N. Harshith |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T13:59:42Z |
| format | Article |
| id | doaj.art-066a8362ac2544999cbb2f6f8a1d4f40 |
| institution | Directory Open Access Journal |
| issn | 2666-1543 |
| language | English |
| last_indexed | 2024-03-07T13:59:42Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Agriculture and Food Research |
| spelling | doaj.art-066a8362ac2544999cbb2f6f8a1d4f402024-03-07T05:29:59ZengElsevierJournal of Agriculture and Food Research2666-15432024-03-0115101020Memory based neural network for cumin price forecasting in Gujarat, IndiaN. Harshith0Prity Kumari1B. A. College of Agriculture, Anand Agricultural University, Anand, Gujarat, IndiaCollege of Horticulture, Anand Agricultural University, Anand, Gujarat, India; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2666154324000577DNNRNNGRULSTM and price forecasting |
| spellingShingle | N. Harshith Prity Kumari Memory based neural network for cumin price forecasting in Gujarat, India Journal of Agriculture and Food Research DNN RNN GRU LSTM and price forecasting |
| title | Memory based neural network for cumin price forecasting in Gujarat, India |
| title_full | Memory based neural network for cumin price forecasting in Gujarat, India |
| title_fullStr | Memory based neural network for cumin price forecasting in Gujarat, India |
| title_full_unstemmed | Memory based neural network for cumin price forecasting in Gujarat, India |
| title_short | Memory based neural network for cumin price forecasting in Gujarat, India |
| title_sort | memory based neural network for cumin price forecasting in gujarat india |
| topic | DNN RNN GRU LSTM and price forecasting |
| url | http://www.sciencedirect.com/science/article/pii/S2666154324000577 |
| work_keys_str_mv | AT nharshith memorybasedneuralnetworkforcuminpriceforecastingingujaratindia AT pritykumari memorybasedneuralnetworkforcuminpriceforecastingingujaratindia |