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Machine Learning Strategies for Time Series Forecasting

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Business Intelligence (eBISS 2012)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 138))

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Abstract

The increasing availability of large amounts of historical data and the need of performing accurate forecasting of future behavior in several scientific and applied domains demands the definition of robust and efficient techniques able to infer from observations the stochastic dependency between past and future. The forecasting domain has been influenced, from the 1960s on, by linear statistical methods such as ARIMA models. More recently, machine learning models have drawn attention and have established themselves as serious contenders to classical statistical models in the forecasting community. This chapter presents an overview of machine learning techniques in time series forecasting by focusing on three aspects: the formalization of one-step forecasting problems as supervised learning tasks, the discussion of local learning techniques as an effective tool for dealing with temporal data and the role of the forecasting strategy when we move from one-step to multiple-step forecasting.

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Authors and Affiliations

  1. Machine Learning Group, Computer Science Department, Faculty of Sciences, ULB, Université Libre de Bruxelles, Bd Triomphe, 1050, Brussels, Belgium

    Gianluca Bontempi, Souhaib Ben Taieb & Yann-Aël Le Borgne

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  1. Gianluca Bontempi
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  2. Souhaib Ben Taieb
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  3. Yann-Aël Le Borgne
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Editors and Affiliations

  1. MAS Laboratory, Ecole Centrale Paris, Châtenay-Malabry, France

    Marie-Aude Aufaure

  2. Department of Computer and Decision Engineering (CoDE), Université Libre de Bruxelles, Brussels, Belgium

    Esteban Zimányi

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Bontempi, G., Ben Taieb, S., Le Borgne, YA. (2013). Machine Learning Strategies for Time Series Forecasting. In: Aufaure, MA., Zimányi, E. (eds) Business Intelligence. eBISS 2012. Lecture Notes in Business Information Processing, vol 138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36318-4_3

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  • DOI: https://doi.org/10.1007/978-3-642-36318-4_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36317-7

  • Online ISBN: 978-3-642-36318-4

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