Machine learning and forecasting a review
The proliferation of business data and on-demand computing have propelled the use of artificial intelligence methods in quantitative forecasting. Machine learning has a prominent role in solving clustering and classification problems as well as dimensionality reduction. Nevertheless, traditional sta...
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| Veröffentlicht in: | Applied economics in the digital era |
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| 1. Verfasser: | |
| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
2020
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Tag hinzufügen | Zusammenfassung: | The proliferation of business data and on-demand computing have propelled the use of artificial intelligence methods in quantitative forecasting. Machine learning has a prominent role in solving clustering and classification problems as well as dimensionality reduction. Nevertheless, traditional statistical methods of forecasting continue to perform well in competitions and many practical applications. The chapter considers critically the successes of machine learning in forecasting, using some case studies as well as theoretical considerations, including limitations on machine learning and other techniques for forecasting. It also discusses weaknesses of the Vapnik-Chervonenkis theory. The main aim of the chapter is to stimulate scholarly dialogue on the role of machine learning in forecasting. |
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| ISBN: | 9783030406004 |