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Agricultural Price Forecasting using Decomposition-based Hybrid Model.
- Source :
- Quarterly Research Journal of Plant & Animal Sciences / Bhartiya Krishi Anusandhan Patrika; Mar2022, Vol. 37 Issue 1, p18-22, 5p
- Publication Year :
- 2022
-
Abstract
- Agricultural price information needs for decision-making at all levels are increasing due to globalization and market integration. Due to its great reliance on biological processes, agricultural price forecasting is one of the most difficult fields of time series analysis. In this paper, a neural network model based on empirical mode decomposition is used to forecast potato prices. The monthly wholesale price series of potato from Chennai market was decomposed into five independent intrinsic modes (IMFs) and one residual with various frequencies. Then, to forecast these IMFs and residual components independently, an artificial neural network with a single hidden layer was built. Finally, the ensemble output for the original price series is formed by aggregating the forecast outcomes of all IMFs, including residuals. In terms of root mean square error and directional prediction statistics, empirical data show that the suggested ensemble model outperforms a single model. [ABSTRACT FROM AUTHOR]
Details
- Language :
- Hindi
- ISSN :
- 03033821
- Volume :
- 37
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Quarterly Research Journal of Plant & Animal Sciences / Bhartiya Krishi Anusandhan Patrika
- Publication Type :
- Academic Journal
- Accession number :
- 158127478
- Full Text :
- https://doi.org/10.18805/BKAP435