Application of Biologically Inspired Methods to Improve Adaptive Ensemble Learning. - Gabriela Grmanová; Viera Rozinajová; Anna Bou Ezzeddine; Mária Lucká; Peter Lacko; Marek Lóderer; Petra Vrablecová; Peter Laurinec (NaBIC, 2015)

Adaptive Time Series Forecasting of Energy Consumption Using Optimized Cluster Analysis. - Peter Laurinec; Marek Lóderer; Petra Vrablecová; Mária Lucká; Viera Rozinajová; Anna Bou Ezzeddine (ICDM Workshops, 2016)

Using biologically inspired computing to effectively improve prediction models. - Anna Bou Ezzeddine; Marek Lóderer; Peter Laurinec; Petra Vrablecová; Viera Rozinajová; Mária Lucká; Peter Lacko; Gabriela Grmanová (Int. J. Hybrid Intell. Syst., 2016)

Usefulness of Unsupervised Ensemble Learning Methods for Time Series Forecasting of Aggregated or Clustered Load. - Peter Laurinec; Mária Lucká (NFMCP@PKDD/ECML, 2017)

Application of Parallel Genetic Algorithm for Model-Based Gaussian Cluster Analysis. - Peter Laurinec; Tomás Jarábek; Mária Lucká (IBICA, 2018)

Clustering-based forecasting method for individual consumers electricity load using time series representations. - Peter Laurinec; Mária Lucká (Open Comput. Sci., 2018)

TSrepr R package: Time Series Representations. - Peter Laurinec (J. Open Source Softw., 2018)

Interpretable multiple data streams clustering with clipped streams representation for the improvement of electricity consumption forecasting. - Peter Laurinec; Mária Lucká (Data Min. Knowl. Discov., 2019)

Density-based unsupervised ensemble learning methods for time series forecasting of aggregated or clustered electricity consumption. - Peter Laurinec; Marek Lóderer; Mária Lucká; Viera Rozinajová (J. Intell. Inf. Syst., 2019)