Machine Learning and Forecasting: A Review
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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.
This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 731143 for Computing with Infinite Data.
The author is grateful to the late Gary Madden for innumerable stimulating conversations on many topics that have influenced the author’s thinking and also to Patricia Madden for much kindness and hospitality in Perth and to both of them for friendship and good cheer in many locations around the world.
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