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Lama, Achal
- Agricultural Price Forecasting Using NARX Model for Soybean Oil
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PDF Views:56
Authors
Affiliations
1 ICAR-Indian Agricultural Research Institute, PUSA, New Delhi 110 012, IN
2 ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110 012, IN
1 ICAR-Indian Agricultural Research Institute, PUSA, New Delhi 110 012, IN
2 ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110 012, IN
Source
Current Science, Vol 125, No 1 (2023), Pagination: 79-84Abstract
The non-linear, non-stationary and complicated nature of agricultural price series makes their accurate forecasting extremely challenging. In comparison to standard statistical methods, artificial neural networks (ANN) have demonstrated promising results for predicting such series. However, the incorporation of auxiliary information can improve prediction accuracy if it is closely linked to the target series. A dynamical neural architecture called a non-linear autoregressive model with exogenous input (NARX) carefully makes use of the auxiliary information to construct a data-dependent non-linear forecasting model. The study explores the performance of NARX model for the real price series of soybean oil (soybean) using soybean (soybean oil) price as exogenous inputs. NARX models outperform ARIMA, ARIMAX and ANN models in terms of RMSE, MAPE, MASE and directional statistics as evaluation criteria. Further, the Diebold-Mariano test confirms a significant improvement in its predictive accuracy.Keywords
Artificial Neural Networks, Mean Absolute Scaled Error, NARX, Price Forecasting, Soybean Oil.References
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- Evaluating the Performance of Crop Yield Forecasting Models Coupled with Feature Selection in Regression Framework
Abstract Views :46 |
PDF Views:42
Authors
Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
Source
Current Science, Vol 125, No 6 (2023), Pagination: 649-654Abstract
As crop yield is determined by numerous input parameters, it is important to identify the most important variables/parameters and eliminate those that may reduce the accuracy of the prediction models. The feature selection algorithms assist in selecting only those relevant features for the prediction algorithms. Instead of a complete set of features, feature subsets give better results for the same algorithm with less computational time. Feature selection has the potential to play an important role in the agriculture domain, with the crop yield depending on multiple factors such as land use, water management, fertilizer application, other management practices and weather parameters. In the present study, feature selection algorithms such as forward selection, backward selection, random forest (RF) and least absolute shrinkage and selection operator (LASSO) have been applied to three different datasets. Regression forecasting models have been developed with selected features for all the algorithms. The forecasting performance of the proposed models was compared using statistical measures such as root mean square error, mean absolute prediction error and mean absolute deviation. A comparison was made among all the feature selection algorithms. The regression models developed with LASSO, RF and backward selection algorithms were the best for different datasets.Keywords
Crop Yield, Feature Selection, Prediction Models, Regression Framework, Statistical Measures, Weather Indices.References
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