KRISHI
ICAR RESEARCH DATA REPOSITORY FOR KNOWLEDGE MANAGEMENT
(An Institutional Publication and Data Inventory Repository)
"Not Available": Please do not remove the default option "Not Available" for the fields where metadata information is not available
"1001-01-01": Date not available or not applicable for filling metadata infromation
"1001-01-01": Date not available or not applicable for filling metadata infromation
Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/84451
Title: | N‑BEATS Deep Learning Architecture for Agricultural Commodity Price Forecasting |
Other Titles: | Not Available |
Authors: | G. H. Harish Nayak Md Wasi Alam G. Avinash K. N. Singh Mrinmoy Ray Rajeev Ranjan Kumar |
ICAR Data Use Licennce: | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf |
Author's Affiliated institute: | ICAR::Indian Agricultural Statistics Research Institute ICAR::Indian Agricultural Research Institute |
Published/ Complete Date: | 2024-08-13 |
Project Code: | Not Available |
Keywords: | Basis expansion · Convolutional neural network · Deep learning · Gated recurrent unit · Long short-term memory |
Publisher: | Springer |
Citation: | Not Available |
Series/Report no.: | Not Available; |
Abstract/Description: | Agricultural commodity prices have unique characteristics and tend to fluctuate more due to seasonality, inelastic demand, and production uncertainty. Additionally, the considerable volatility observed in time series data amplifies the complexity, presenting a notable challenge. This paper addresses the intricate challenges asso ciated with forecasting agricultural commodity prices, which are characterized by seasonality, inelastic demand, and production uncertainty. We introduce deep learn ing (DL) models to navigate the complexities of nonlinear and nonstationary price data in the agricultural sector. Despite the success of DL models in handling intri cate data, their original design for tasks like image processing and natural language processing necessitates specialized architectures for time series forecasting. To meet this demand, we evaluate the neural basis expansion analysis for interpretable time series forecasting (N-BEATS) model, a novel architecture designed specifically for time series forecasting, on weekly potato price data collected from the Farrukhabad market in Uttar Pradesh between January 2003 and August 2023. A comparative analysis is conducted with three other models: convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent units (GRU) using the same dataset. Various forecasting evaluation criteria, including root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), are employed to assess and compare the models’ performance. Empirical results demonstrate that the N-BEATS model consistently outperforms the other models across all evaluation criteria. Furthermore, the Diebold–Mariano (DM) test confirms the significant forecasting advantage of the N-BEATS model over the other sequen tial models. This research showcases the potential of the N-BEATS model in enhanc ing the precision of agricultural commodity price forecasting, with implications for stakeholders such as farmers and planners. The findings contribute to advancing the understanding of deep learning applications in the agricultural domain, offering a promising avenue for more accurate and reliable forecasting methods. |
Description: | Not Available |
ISSN: | Not Available |
Type(s) of content: | Research Paper |
Sponsors: | Not Available |
Language: | English |
Name of Journal: | Potato Research |
Journal Type: | Included NAAS journal list |
NAAS Rating: | 8.3 |
Impact Factor: | Not Available |
Volume No.: | NA |
Page Number: | 1-21 |
Name of the Division/Regional Station: | Not Available |
Source, DOI or any other URL: | https://doi.org/10.1007/s11540-024-09789-y |
URI: | http://krishi.icar.gov.in/jspui/handle/123456789/84451 |
Appears in Collections: | AEdu-IASRI-Publication |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Harish_2024_PR.pdf | 2.38 MB | Adobe PDF | View/Open |
Items in KRISHI are protected by copyright, with all rights reserved, unless otherwise indicated.