A5080182531- Agricultural Economics and Practices
- Helminth infection and control
- Stock Market Forecasting Methods
- Energy Load and Power Forecasting
- Energy, Environment, and Transportation Policies
- Forecasting Techniques and Applications
- Livestock Management and Performance Improvement
- Agricultural Science and Fertilization
- Gender, Labor, and Family Dynamics
- Fiscal Policy and Economic Growth
- Market Dynamics and Volatility
- Genetics and Plant Breeding
- Spectroscopy and Chemometric Analyses
- Rice Cultivation and Yield Improvement
- Electric Vehicles and Infrastructure
- Hydrological Forecasting Using AI
- Neural Networks and Applications
- Global Health Care Issues
- Coccidia and coccidiosis research
- Genetic and phenotypic traits in livestock
- Wheat and Barley Genetics and Pathology
- Agricultural Systems and Practices
- Monetary Policy and Economic Impact
- Energy and Environment Impacts
- Vector-borne infectious diseases
Indian Agricultural Statistics Research Institute
2018-2025
Government of Haryana
2025
Indian Institute of Sugarcane Research
2025
Indian Agricultural Research Institute
2022-2024
Indian Institute of Vegetable Research
2024
Hemwati Nandan Bahuguna Garhwal University
2018-2024
Narendra Dev University of Agriculture and Technology
2017-2024
Graphic Era University
2024
Pandit Bhagwat Dayal Sharma Post Graduate Institute of Medical Sciences
2024
The Sanskrit College and University
2024
Accurately predicting agricultural commodity prices is crucial for India's economy. Traditional parametric models struggle with stringent assumptions, while machine learning (ML) approaches, though data-driven, lack automatic feature extraction. Deep (DL) models, advanced extraction and predictive abilities, offer a promising solution. However, their application to price data ignored the exogenous factors. Hence, study explored versions of well-known univariate NBEATSX TransformerX. The...
Accurately predicting agricultural commodity prices is challenging due to their unpredictable and complex nature. Existing models often fail capture nonlinear nonstationary patterns in price data, resulting less accurate forecasts. To tackle these challenges, we present a novel hybrid VMD-LSTM model that synergistically combines genetic algorithm (GA), variational mode decomposition (VMD), long short-term memory (LSTM), leading better prediction accuracy. The proposed utilizes GA-optimized...