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"EMD-SVR" Hybrid Machine Learning Model and its Application in Agricultural Price Forecasting

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Title "EMD-SVR" Hybrid Machine Learning Model and its Application in Agricultural Price Forecasting
Not Available
 
Creator Pankaj Das
Girish Kumar Jha
Achal Lama
Bharti
 
Subject Agricultural price forecasting
Empirical mode decomposition
Nonlinearity
Nonstationary
Support vector regression
 
Description Not Available
Timely and accurate price forecasting is one of challenges in agriculture. It helps both producer and consumer to make the efficient plan. The inherent nonstationarity and nonlinearity in price data makes problems in forecasting. A single forecasting model may not be able to tackle nonstationarity and nonlinearity, simultaneously. With this context, a nonlinear hybrid model called EMD-SVR has been proposed to deal the problem. The empirical mode decomposition (EMD) deals with nonstationarity by decomposing price data into a finite and small number of subsets. Further, these decomposed subsets are forecasted using Support Vector Regression (SVR) model and aggregated to make final forecast. The performance of the proposed hybrid model are evaluated in monthly price index of chili. The empirical results indicated the superiority of the EMD-SVR model.
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Date 2022-04-19T06:38:13Z
2022-04-19T06:38:13Z
2022-04-16
 
Type Article
 
Identifier Das, P., Jha, G.K., Lama, A. and Bharti (2022). “EMD-SVR” Hybrid Machine Learning Model and its Application in Agricultural Price Forecasting. Bhartiya Krishi Anusandhan Patrika. DOI: 10.18805/BKAP385.
http://krishi.icar.gov.in/jspui/handle/123456789/71621
 
Language Hindi
 
Relation Not Available;
 
Publisher ARCC