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Kumar, Rajeev Ranjan
- A Hybrid Approach for Forecasting Mustard Price having Long-Memory Property
Abstract Views :108 |
PDF Views:66
Authors
Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
2 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
2 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN
Source
Current Science, Vol 124, No 5 (2023), Pagination: 632-635Abstract
For the modelling of time series data having long memory properties, we generally use the autoregressive fractionally integrated moving average (ARFIMA) model. This model performs well compared to the autoregressive integrated moving average (ARIMA) model. However, it cannot capture the nonlinear property of the data. In order to achieve the desired and accurate forecasts, hybridizing the existing forecasting models is an important technique. The hybrid time-series model combines the strength of individual models. Accordingly, this study proposes a hybrid model based on ARFIMA and extreme learning machine (ELM) for agricultural time-series data with long memory properties. For evaluation of the proposed model, the daily mustard price (₹/q) of Agra and Bharatpur markets from 1 January 2016 to 31 January 2020 was used. Empirical results show that the forecasting performance of the proposed hybrid model based on ARFIMA and ELM is better than the existing models.Keywords
Hybrid Model, Long Memory, Mustard, Price Forecasting, Time-Series Data.Full Text
References
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- Agricultural Price Forecasting Using NARX Model for Soybean Oil
Abstract Views :96 |
PDF Views:60
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.Full Text
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