Machine learning strategies for time series forecasting

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

ULB Institutional Repository from ULB -- Universite Libre de Bruxelles

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. © 2013 Springer-Verlag.

Keywords: lazy learning; local learning; machine learning; MIMO; Time series forecasting (search for similar items in EconPapers)
Date: 2013
Note: SCOPUS: cp.k
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Citations: View citations in EconPapers (29)

Published in: Lecture Notes in Business Information Processing (2013) v.138 LNBIP,p.62-77

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