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Current IssueEditorial BoardOnline First Articles
Bhartiya Krishi Anusandhan Patrika
Print ISSN: 0303-3821
Online ISSN: 0976-4631
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4.95

Chief Editor:
Yashpal Singh Malik
ICAR-Indian Veterinary Research Institute (ICAR-IVRI)
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Full Research Article

Bhartiya Krishi Anusandhan Patrika, volume 38 issue 3 (september 2023) : 218-222

Agricultural Commodity Price Prediction using Long Short-Term Memory (LSTM) based Neural Networks

Ronit Jaiswal1,2, Girish K. Jha1,*, Kapil Choudhary1, Rajeev Ranjan 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.

  • Submitted24-11-2022|

  • Accepted28-08-2023|

  • First Online 14-10-2023|

  • doi 10.18805/BKAP613

Cite article:- Jaiswal Ronit, Jha K. Girish, Choudhary Kapil, Kumar Ranjan Rajeev (2023). Agricultural Commodity Price Prediction using Long Short-Term Memory (LSTM) based Neural Networks . Bhartiya Krishi Anusandhan Patrika. 38(3): 218-222. doi: 10.18805/BKAP613.

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.

REFERENCES


  1. Banerjee, R., Das, P., Ahmad, T. and Kumar, M. (2021). Modeling and forecasting of agricultural commodity production under changing climatic condition: A review. Bhartiya Krishi Anusandhan Patrika. 36(4): 273-279.

  2. Bengio, Y., Simard, P. and Frasconi, P. (1994). Learning long- term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks. 5(2): 157-166.

  3. Box, G.E.P. and Jenkins, G.M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco, CA.

  4. Clements, M.P. and Smith, J. (1997). The performance of alternative forecasting methods for SETAR models.  International Journal of Forecasting. 13(4): 463-475.

  5. Choudhary, K., Jha, G.K. and Kumar, R.R. (2019). Delhi potato price analysis using ensemble empirical mode decomposition. Bharatiya Krishi Anusandhan Patrika. 34(1): 33-37.

  6. Jaiswal, R., Jha, G.K., Kumar, R.R. and Choudhary, K. (2021).  Deep long short-term memory based model for agricultural price forecasting. Neural Computing and Applications. 34(6): 4661-4676.

  7. Jha, Girish, K. and Sinha, K. (2013). Agricultural price forecasting using neural network model: An innovative information delivery system. Agricultural Economics Research Review. 26(2): 229-239. 

  8. Kumar, R.R., Jha, G.K., Choudhary, K. and Budhlakoti, N. (2019). Examining integration between Agra and Delhi potato markets. Bhartiya Krishi Anusandhan Patrika. 34(1): 62-64.

  9. LeCun, Y., Bengio, Y. and Hinton, G. (2015). Deep learning.  Nature. 521(7553): 436-444. 
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