Record Details

Variational Mode Decomposition based Machine Learning Models Optimized with Genetic Algorithm for Price Forecasting

KRISHI: Publication and Data Inventory Repository

View Archive Info
 
 
Field Value
 
Title Variational Mode Decomposition based Machine Learning Models Optimized with Genetic Algorithm for Price Forecasting
Not Available
 
Creator Pankaj Das
Achal Lama
Girish Kumar Jha
 
Subject Price forecasting
Machine learning
VMD
Genetic Algorithm
Support vector regression
Random Forest
 
Description Not Available
Accurate and timely price information and forecasting help in making efficient plans and strategies. Non-linearity and non-stationarity behaviour of
price data create problems in price forecasting. In this paper, variational mode decomposition (VMD) based optimised genetic algorithm (GA) hybrid
machine learning (ML) models have been proposed. The VMD algorithm is employed to decompose the price data into intrinsic mode functions
(IMFs) which is further forecasted using ML models namely support vector regression (SVR) and random forest (RF). The practical use of the SVR
and RF models is limited because the accuracy of ML models heavily depends on a proper setting of hyper-parameters. Therefore, these model
hyper-parameters are optimized using GA. Further, the forecasted values of IMFs through the GA optimised SVR and RF are aggregated for the final
forecast. The results of the proposed model are benchmarked with the comparative models. The proposed VMD-GA-RF and VMD-GA-SVR models
are tested on the weekly onion price of the Delhi and Nashik market. The results clearly demonstrate that the combination of VMD and GA optimized
models can improve the performance of the prediction of the dataset.
Not Available
 
Date 2023-04-03T05:03:43Z
2023-04-03T05:03:43Z
2023-02-28
 
Type Article
 
Identifier Not Available
0019-6363
http://krishi.icar.gov.in/jspui/handle/123456789/76648
 
Language English
 
Relation Not Available;
 
Publisher Indian Society of Agricultural Statistics