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International Journal of Data Science and Analytics
Erschienen in: International Journal of Data Science and Analytics 4/2019

31.08.2018 | Regular Paper

Batch and incremental dynamic factor machine learning for multivariate and multi-step-ahead forecasting

verfasst von: Jacopo De Stefani, Yann-Aël Le Borgne, Olivier Caelen, Dalila Hattab, Gianluca Bontempi

Erschienen in: International Journal of Data Science and Analytics | Ausgabe 4/2019

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Abstract

Most multivariate forecasting methods in the literature are restricted to vector time series of low dimension, linear methods and short horizons. Big data revolution is instead shifting the focus to problems (e.g., issued from the IoT technology) characterized by very large dimension, nonlinearity and long forecasting horizons. This paper discusses and compares a set of state-of-the-art methods which could be promising in tackling such challenges. Also, it proposes DFML, a machine-learning version of the dynamic factor model, a successful forecasting methodology well-known in econometrics. The DFML strategy is based on a out-of-sample selection of the nonlinear forecaster, the number of latent components and the multi-step-ahead strategy. We will discuss both a batch and an incremental version of DFML, and we will show that it can consistently outperform state-of-the-art methods in a number of Synthetic and real forecasting tasks.
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Fußnoten
1
In the following of the paper, we will assume \(d=0\) for the sake of simplicity.
 
2
In batch PCA the components are recomputed at each instant t while in incremental they are updated sequentially (after initialization with a small data subset).
 
3
The experimental sessions can be reproduced by means of the R code available in the github repository https://​github.​com/​gbonte/​panel.
 
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Metadaten
Titel
Batch and incremental dynamic factor machine learning for multivariate and multi-step-ahead forecasting
verfasst von
Jacopo De Stefani
Yann-Aël Le Borgne
Olivier Caelen
Dalila Hattab
Gianluca Bontempi
Publikationsdatum
31.08.2018
Verlag
Springer International Publishing
Erschienen in
International Journal of Data Science and Analytics / Ausgabe 4/2019
Print ISSN: 2364-415X
Elektronische ISSN: 2364-4168
DOI
https://doi.org/10.1007/s41060-018-0150-x

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