Forecasting agricultural commodities prices using deep learning-based models: basic LSTM, bi-LSTM, stacked LSTM, CNN LSTM, and convolutional LSTM
by R. Murugesan; Eva Mishra; Akash Hari Krishnan
International Journal of Sustainable Agricultural Management and Informatics (IJSAMI), Vol. 8, No. 3, 2022

Abstract: The literature argues that an accurate price prediction of agricultural goods is quintessential to ensure the economy's good functioning worldwide. Research reveals that studies with the application of deep learning in the tasks of agricultural price forecast on short historical agricultural price data are very scarce. The gap mentioned above is removed in this study by employing five versions of LSTM deep learning techniques for five agricultural commodities prices prediction on a univariate time series dataset of rice, wheat, gram, banana, and groundnut spanning January 2000 to July 2020. The study obtained good forecasting results for all five commodities employing the five LSTM models. The study validated the results with lower values of error metrics, MAE, MAPE, MSE, and RMSE, and two paired t-tests with hypothesis and confidence levels of 95% as a measure of robustness. The study predicted one month ahead future price and compared it with actual prices using LSTM models.

Online publication date: Tue, 27-Sep-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Sustainable Agricultural Management and Informatics (IJSAMI):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com