Record Details

Empirical Mode Decomposition Based Ensemble Hybrid Machine Learning Models for Agricultural Commodity Price Forecasting

KRISHI: Publication and Data Inventory Repository

View Archive Info
 
 
Field Value
 
Title Empirical Mode Decomposition Based Ensemble Hybrid Machine Learning Models for Agricultural Commodity Price Forecasting
Not Available
 
Creator Pankaj Das
Girish Kumar Jha
Achal Lama
 
Subject Agricultural commodity price
Machine learning
Empirical mode decomposition
Nonlinearity
Nonstationary
Artificial neural network
Support vector regression
 
Description Not Available
Agricultural commodity price is very volatile in nature due to its nonlinearity and
nonstationary character. The volatility behaviour of the commodity price creates a lot of problems
for both producer and consumer. The steady forecast of the price may reduce the problems and
increase the profit for the stakeholders. In this study, an ensemble hybrid machine learning model
based on empirical mode decomposition (EMD) has been proposed to forecast the commodity
price. EMD decomposes the nonstationary and nonlinear price series into different stationary
intrinsic mode functions (IMF) and a final residue. Then Machine learning techniques like
Artificial neural network (ANN) and Support vector regression (SVR) were used to forecast each
of the decomposed components. Finally, all the forecasted values of the decomposed components
were aggregated to produce the final forecast. Two R modules were developed for the application
of the proposed methodology. The proposed methodology has been applied to the monthly
wholesale price index of vegetables. The results indicated that the ensemble hybrid machine
learning model based on empirical mode decomposition has superior performance compared to
generic models
Not Available
 
Date 2023-05-26T08:28:56Z
2023-05-26T08:28:56Z
2023-05-08
 
Type Article
 
Identifier Das, Pankaj & Jha, Girish & Lama, Achal. (2023). Empirical Mode Decomposition Based Ensemble Hybrid Machine Learning Models for Agricultural Commodity Price Forecasting. Statistics and Applications. 21. 99-112.
2454-7395
http://krishi.icar.gov.in/jspui/handle/123456789/77772
 
Language English
 
Relation Not Available;
 
Publisher Society of Statistics, Computer and Applications