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Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/82091
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ranjit Kumar Paul | en_US |
dc.contributor.author | Tanima Das | en_US |
dc.contributor.author | Md. Yeasin | en_US |
dc.date.accessioned | 2024-04-15T12:32:32Z | - |
dc.date.available | 2024-04-15T12:32:32Z | - |
dc.date.issued | 2023-02-01 | - |
dc.identifier.citation | Not Available | en_US |
dc.identifier.issn | Not Available | - |
dc.identifier.uri | http://krishi.icar.gov.in/jspui/handle/123456789/82091 | - |
dc.description | Not Available | en_US |
dc.description.abstract | Forecasting price volatility of agricultural commodities has immense importance nowadays. The use of traditional parametric model in capturing volatility in price series has been found to be inefficient. In this context, machine learning (ML) technique like support vector regression (SVR) may be applied to improve accuracy of forecasting. In the present investigation, an algorithm based on combination of parametric nonlinear time series model, i.e., generalized autoregressive conditional heteroscedastic (GARCH) model and supervised ML, e.g., SVR is proposed. The method is applied for forecasting volatility of onion price in two major markets of India, namely Delhi and Kolkata. The outperformance of the proposed algorithm in comparison to GARCH model has also been empirically established by means of Root Mean Square Error, Mean Absolute Error and R2 log. | en_US |
dc.description.sponsorship | Not Available | en_US |
dc.language.iso | English | en_US |
dc.publisher | Not Available | en_US |
dc.relation.ispartofseries | Not Available; | - |
dc.subject | Forecasting | en_US |
dc.subject | support vector regression | en_US |
dc.subject | nonlinear time series model | en_US |
dc.title | Ensemble of Time Series and Machine Learning Model for Forecasting Volatility in Agricultural Prices | 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 | National Science Academy Science Letters | en_US |
dc.publication.volumeno | 46(3) | en_US |
dc.publication.pagenumber | Not Available | en_US |
dc.publication.divisionUnit | Not Available | en_US |
dc.publication.sourceUrl | DOI: 10.1007/s40009-023-01218-x | en_US |
dc.publication.authorAffiliation | ICAR::Indian Agricultural Statistics Research Institute | en_US |
dc.ICARdataUseLicence | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf | en_US |
dc.publication.journaltype | National Science Academy Science Letter | en_US |
dc.publication.naasrating | Not Available | en_US |
dc.publication.impactfactor | 1.1 | en_US |
Appears in Collections: | AEdu-IASRI-Publication |
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