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Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/80951
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kapil Choudhary | en_US |
dc.contributor.author | Girish K. Jha | en_US |
dc.contributor.author | Ronit Jaiswal | en_US |
dc.contributor.author | P. Venkatesh | en_US |
dc.contributor.author | Rajender Parsad | en_US |
dc.date.accessioned | 2023-11-30T07:11:46Z | - |
dc.date.available | 2023-11-30T07:11:46Z | - |
dc.date.issued | 2023-11-28 | - |
dc.identifier.citation | Kapil Choudhary, Girish K. Jha, Ronit Jaiswal, P. Venkatesh and Rajender Parsad (2023). Agricultural Price Forecasting Based on Variational Mode Decomposition and Time-Delay Neural Network. Statistics and Applications, 21(2), 237-259. https://ssca.org.in/media/14_SA44052022_R3_SA_21032023_Girish_Jha_FINAL_Finally.pdf | en_US |
dc.identifier.issn | Not Available | - |
dc.identifier.uri | http://krishi.icar.gov.in/jspui/handle/123456789/80951 | - |
dc.description | Not Available | en_US |
dc.description.abstract | Agricultural commodities prices are very unpredictable and complex, and thus, forecasting these prices is one of the research hotspots. In this paper, we propose a new hybrid VMD-TDNN model combining variational mode decomposition (VMD) and time-delay neural network (TDNN) to improve the accuracy of agricultural price forecasting. Specifically, the VMD decomposes a price series into a set of intrinsic mode functions (IMFs), and the obtained IMFs are modelled and forecasted separately using the TDNN models. Finally, the forecasts of all IMFs are combined to provide an ensemble output for the price series. VMD overcomes the limitation of the mode mixing and end effect problems of the empirical mode decomposition (EMD) based variants. The prediction ability of the proposed model is compared with TDNN, and EMD based variants coupled with TDNN model using international monthly price series of maize, palm oil, and soybean in terms of evaluation criteria like root mean squared error, mean absolute percentage error and, directional prediction statistics. Additionally, Diebold-Mariano test and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a ranking system, are used to evaluate the accuracy of the models. The empirical results confirm that the proposed hybrid model is superior in terms of evaluation criteria and improves the prediction accuracy significantly. | en_US |
dc.description.sponsorship | Not Available | en_US |
dc.language.iso | English | en_US |
dc.publisher | Statistics and Applications, Society of Statistics and Computer Applications | en_US |
dc.relation.ispartofseries | Not Available; | - |
dc.subject | Agricultural price forecasting | en_US |
dc.subject | Empirical mode decomposition | en_US |
dc.subject | Intrinsic mode function | en_US |
dc.subject | Time-delay neural network | en_US |
dc.subject | Variational mode decomposition | en_US |
dc.title | Agricultural Price Forecasting Based on Variational Mode Decomposition and Time-Delay Neural Network | en_US |
dc.title.alternative | Not Available | en_US |
dc.type | Research Paper | en_US |
dc.publication.projectcode | Not Available | en_US |
dc.publication.journalname | Statistics and Applications | en_US |
dc.publication.volumeno | 21(2) | en_US |
dc.publication.pagenumber | 237-259 | en_US |
dc.publication.divisionUnit | Not Available | en_US |
dc.publication.sourceUrl | https://ssca.org.in/media/14_SA44052022_R3_SA_21032023_Girish_Jha_FINAL_Finally.pdf | en_US |
dc.publication.authorAffiliation | ICAR::Indian Agricultural Statistics Research Institute | en_US |
dc.publication.authorAffiliation | ICAR::Indian Agricultural Research Institute | en_US |
dc.ICARdataUseLicence | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf | en_US |
dc.publication.journaltype | NAAS | en_US |
dc.publication.naasrating | Not Available | en_US |
dc.publication.impactfactor | Not Available | en_US |
Appears in Collections: | AEdu-IASRI-Publication |
Files in This Item:
File | Description | Size | Format | |
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KapilGKJhaRpSA2023.pdf | 981.9 kB | Adobe PDF | View/Open |
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