par Bontempi, Gianluca 
;Le Borgne, Yann-Aël ;De Stefani, Jacopo
Référence 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), page (222-231)
Publication Publié, 2017-10
;Le Borgne, Yann-Aël ;De Stefani, Jacopo Référence 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), page (222-231)
Publication Publié, 2017-10
Publication dans des actes
| Résumé : | 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 horizon. 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 (DFM), 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 show that DFML can consistently outperform state-of-the-art methods in a number of synthetic and real forecasting tasks. |
