| Incremental ensemble learning for electricity load forecasting G Grmanová, P Laurinec, V Rozinajová, AB Ezzeddine, M Lucká, P Lacko, ... Acta Polytechnica Hungarica 13 (2), 97-117, 2016 | 56 | 2016 |
| Interpretable multiple data streams clustering with clipped streams representation for the improvement of electricity consumption forecasting P Laurinec, M Lucká Data Mining and Knowledge Discovery 33, 413-445, 2019 | 37 | 2019 |
| Adaptive time series forecasting of energy consumption using optimized cluster analysis P Laurinec, M Lóderer, P Vrablecová, M Lucká, V Rozinajová, ... 2016 IEEE 16th international conference on data mining workshops (ICDMW …, 2016 | 28 | 2016 |
| Energy load forecast using S2S deep neural networks with k-Shape clustering T Jarábek, P Laurinec, M Lucká 2017 IEEE 14th International Scientific Conference on Informatics, 140-145, 2017 | 26 | 2017 |
| Density-based unsupervised ensemble learning methods for time series forecasting of aggregated or clustered electricity consumption P Laurinec, M Lóderer, M Lucká, V Rozinajová Journal of Intelligent Information Systems 53, 219-239, 2019 | 25 | 2019 |
| TSrepr R package: Time Series Representations P Laurinec Journal of Open Source Software 3 (23), 577, 2018 | 25 | 2018 |
| Clustering-based forecasting method for individual consumers electricity load using time series representations P Laurinec, M Lucká Open Computer Science 8 (1), 38-50, 2018 | 24 | 2018 |
| Comparison of representations of time series for clustering smart meter data P Laurinec, M Lucká Proceedings of the world congress on engineering and computer science 1, 2016 | 21 | 2016 |
| New clustering-based forecasting method for disaggregated end-consumer electricity load using smart grid data P Laurinec, M Lucká 2017 IEEE 14th international scientific conference on informatics, 210-215, 2017 | 10 | 2017 |
| Usefulness of unsupervised ensemble learning methods for time series forecasting of aggregated or clustered load P Laurinec, M Lucká New Frontiers in Mining Complex Patterns: 6th International Workshop, NFMCP …, 2018 | 9 | 2018 |
| Application of biologically inspired methods to improve adaptive ensemble learning G Grmanová, V Rozinajová, AB Ezzedine, M Lucká, P Lacko, M Lóderer, ... Advances in Nature and Biologically Inspired Computing: Proceedings of the …, 2016 | 9 | 2016 |
| Using biologically inspired computing to effectively improve prediction models AB Ezzeddine, M Lóderer, P Laurinec, P Vrablecová, V Rozinajová, ... International Journal of Hybrid Intelligent Systems 13 (2), 99-112, 2016 | 6 | 2016 |
| Improving forecasting accuracy through the influence of time series representations and clustering P Laurinec Information Sciences and Technologies 10 (2), 6, 2018 | 5 | 2018 |
| Application of Parallel Genetic Algorithm for Model-Based Gaussian Cluster Analysis P Laurinec, T Jarábek, M Lucká Innovations in Bio-Inspired Computing and Applications: Proceedings of the …, 2019 | | 2019 |
| PARALLEL MULTI-DENSITY BASED CLUSTERING L Csóka, P Laurinec, M Lucká PARNUM 2017 International Workshop on Parallel Numerics April 19–21, 2017 …, 2017 | | 2017 |
Maria Lucka, prof.senior researcher, Kempelen Institute of Intelligent Technologies, Bratislava, SlovakiaVerified email at kinit.sk
