Machine learning strategies for time series forecasting

Gianluca Bontempi, Souhaib Ben Taieb, Yann-Aël Le borgne

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

135 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationBusiness Intelligence
Subtitle of host publicationSecond European Summer School, eBISS 2012, Brussels, Belgium, July 15-21, 2012, Tutorial Lectures
EditorsMarie-Aude Aufaure, Esteban Zimányi
Place of PublicationBerlin Germany
Number of pages16
ISBN (Electronic)9783642363184
ISBN (Print)9783642363177
Publication statusPublished - 2013
Externally publishedYes
Event2nd European Business Intelligence Summer School - Université Libre de Bruxelles, Brussels, Belgium
Duration: 15 Jul 201221 Jul 2012
Conference number: 2nd

Publication series

NameLecture Notes in Business Information Processing
ISSN (Print)1865-1348
ISSN (Electronic)1865-1356


Conference2nd European Business Intelligence Summer School
Abbreviated titleeBISS 2012
Internet address

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