Density-based unsupervised ensemble learning methods for time series forecasting of aggregated or clustered electricity consumption
Crossref DOI link: https://doi.org/10.1007/s10844-019-00550-3
Published Online: 2019-03-16
Published Print: 2019-10
Update policy: https://doi.org/10.1007/springer_crossmark_policy
Laurinec, Peter http://orcid.org/0000-0002-3501-8783
Lóderer, Marek
Lucká, Mária
Rozinajová, Viera
Text and Data Mining valid from 2019-03-16
Article History
Received: 2 August 2018
Revised: 20 February 2019
Accepted: 22 February 2019
First Online: 16 March 2019