OptiSembleForecasting: optimization-based ensemble forecasting using MCS algorithm and PCA-based error index.
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| Title: | OptiSembleForecasting: optimization-based ensemble forecasting using MCS algorithm and PCA-based error index. |
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| Authors: | Yeasin, Md.1 (AUTHOR), Paul, Ranjit Kumar1 (AUTHOR) ranjitstat@gmail.com |
| Source: | Journal of Supercomputing. Jan2024, Vol. 80 Issue 2, p1568-1597. 30p. |
| Subjects: | Stochastic learning models, Deep learning, Standard deviations, Forecasting, Algorithms, Mathematical optimization |
| Geographic Terms: | India |
| Abstract: | Ensemble forecasts from multiple models have gained enormous popularity as it provides a more efficient forecast as compared to the individual counterpart. The linear weighted combination method is most widely utilized for its simplicity and efficiency. Despite hard efforts by various researchers, two considerable challenges still exist: (1) Systematic and robust techniques for selecting suitable forecasting models and (2) Techniques to find appropriate combination weights. To address these challenges a novel framework for optimization-based ensemble technique, 'OptiSembleForecasting' has been proposed in this study. The three components of the proposed framework are (a) Principal Component Analysis-based error index, (b) Model Confidence Set algorithm and (c) Optimization techniques. A total of thirteen forecasting models consisting of five deep learning, five machine learning and three stochastic models and twenty optimization techniques have been implemented in the proposed framework. To examine the effectiveness of the proposed technique, wholesale price of three commodities (TOP: Tomato, Onion, and Potato) each with two major markets in India has been considered. The empirical evaluation of the predictive accuracy of different models with that of the proposed techniques has been carried out by means of root mean square error and mean absolute percentage error. The findings of this study demonstrated the superiority of the proposed algorithm. Moreover, a R-package, namely 'OptiSembleForecasting', has been developed to make the implementation of this technique simple and user-friendly. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Ensemble forecasts from multiple models have gained enormous popularity as it provides a more efficient forecast as compared to the individual counterpart. The linear weighted combination method is most widely utilized for its simplicity and efficiency. Despite hard efforts by various researchers, two considerable challenges still exist: (1) Systematic and robust techniques for selecting suitable forecasting models and (2) Techniques to find appropriate combination weights. To address these challenges a novel framework for optimization-based ensemble technique, 'OptiSembleForecasting' has been proposed in this study. The three components of the proposed framework are (a) Principal Component Analysis-based error index, (b) Model Confidence Set algorithm and (c) Optimization techniques. A total of thirteen forecasting models consisting of five deep learning, five machine learning and three stochastic models and twenty optimization techniques have been implemented in the proposed framework. To examine the effectiveness of the proposed technique, wholesale price of three commodities (TOP: Tomato, Onion, and Potato) each with two major markets in India has been considered. The empirical evaluation of the predictive accuracy of different models with that of the proposed techniques has been carried out by means of root mean square error and mean absolute percentage error. The findings of this study demonstrated the superiority of the proposed algorithm. Moreover, a R-package, namely 'OptiSembleForecasting', has been developed to make the implementation of this technique simple and user-friendly. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 09208542 |
| DOI: | 10.1007/s11227-023-05542-3 |
