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Soft Computing
Erschienen in: Soft Computing 3/2015

01.03.2015 | Methodologies and Application

Time series prediction using sparse regression ensemble based on \(\ell _2\)–\(\ell _1\) problem

verfasst von: Li Zhang, Wei-Da Zhou

Erschienen in: Soft Computing | Ausgabe 3/2015

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Abstract

Sparse regression ensemble (SRE) is to sparsely combine the outputs of multiple learners using a sparse weight vector. This paper deals with SRE based on the \(\ell _2\)–\(\ell _1\) problem and applies it to time series prediction problems. The \(\ell _2\)–\(\ell _1\) problem consists of \(\ell _2\)-norm and \(\ell _1\)-norm regularization terms, where the former denotes the total ensemble empirical risk, and the latter represents the ensemble complexity. Thus, the goal is both to minimize the total ensemble training error and control the ensemble complexity. Experiments on real-world data for regression and time series prediction are given.
Vorheriger Artikel An interest rate model in uncertain environment
Nächster Artikel Application of soft computing techniques for modeling and analysis of MRR and taper in laser machining process as well as weld strength and weld width in laser welding process

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Metadaten
Titel
Time series prediction using sparse regression ensemble based on – problem
verfasst von
Li Zhang
Wei-Da Zhou
Publikationsdatum
01.03.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 3/2015
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-014-1304-y

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