https://doi.org/10.1140/epjst/e2014-02210-3
Regular Article
Predicting multivariate time series in real time with confidence intervals: Applications to renewable energy
1 Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
2 Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 153-8505, Japan
3 CREST, JST, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
a e-mail: yoshito@sat.t.u-tokyo.ac.jp
Received: 27 December 2013
Revised: 6 May 2014
Published online: 24 June 2014
We extend our earlier work on predicting a univariate time series in real time with confidence intervals (Hirata, et al., Renew. Energy 67, 35 (2014)) to a multivariate time series. We realize this extension by using the “p-norm” where p is smaller than 1. We compare the performance when p is 0.5 with that when p is 2 using solar irradiation data and wind data measured all over Japan.
© EDP Sciences, Springer-Verlag, 2014
