NDLI: A Monte Carlo strategy for structured multiple-step-ahead time series prediction

Content Provider	IEEE Xplore Digital Library
Author	Bontempi, G.
Copyright Year	2014
Description	Author affiliation: Machine Learning Group, Univ. Libre de Bruxelles, Brussels, Belgium (Bontempi, G.)
Abstract	Forecasting a time series multiple-step-ahead is a challenging problem for several reasons: the accumulation of errors, the noise, and the complexity of the dependency between past and far future which has to be inferred on the basis of a limited amount of data. Traditional approaches to multi-step-ahead forecasting reduce the problem to a series of single-output prediction tasks. This is notably the case of the Iterated and the Direct approaches. More recently, multiple-output approaches appeared and stressed the multivariate and structured nature of the output to be predicted. This paper intends to go a step further in this direction by formulating the problem of multi-step-ahed forecasting as a problem of conditional multivariate estimation which can be addressed by a Monte Carlo importance sampling strategy. The interesting aspect of the approach is that this probabilistic formulation allows a natural integration of the traditional Iterated and Direct approaches. The extensive assessment of our algorithm with the NN5, NN3 and a synthetic benchmark shows that this approach is promising and competitive with the state-of-the-art.
Starting Page	853
Ending Page	858
File Size	601161
Page Count	6
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889666
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time series analysis Forecasting Monte Carlo methods Estimation Artificial neural networks Vectors Probabilistic logic
Content Type	Text
Resource Type	Article

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