| Evaluating time series forecasting models: An empirical study on performance estimation methods V Cerqueira, L Torgo, I Mozetič Machine Learning 109 (11), 1997-2028, 2020 | 350 | 2020 |
| A case study comparing machine learning with statistical methods for time series forecasting: size matters V Cerqueira, L Torgo, C Soares Journal of Intelligent Information Systems 59 (2), 415-433, 2022 | 185* | 2022 |
| Arbitrated ensemble for time series forecasting V Cerqueira, L Torgo, F Pinto, C Soares Machine Learning and Knowledge Discovery in Databases: European Conference …, 2017 | 85 | 2017 |
| Forecasting hotel demand for revenue management using machine learning regression methods LN Pereira, V Cerqueira Current Issues in Tourism 25 (17), 2733-2750, 2022 | 61 | 2022 |
| Arbitrage of forecasting experts V Cerqueira, L Torgo, F Pinto, C Soares Machine Learning 108, 913-944, 2019 | 51 | 2019 |
| A comparative study of performance estimation methods for time series forecasting V Cerqueira, L Torgo, J Smailović, I Mozetič 2017 IEEE international conference on data science and advanced analytics …, 2017 | 50 | 2017 |
| Vest: Automatic feature engineering for forecasting V Cerqueira, N Moniz, C Soares Machine Learning, 1-23, 2021 | 42 | 2021 |
| How to evaluate sentiment classifiers for Twitter time-ordered data? I Mozetič, L Torgo, V Cerqueira, J Smailović PloS one 13 (3), e0194317, 2018 | 42 | 2018 |
| SMOTEBoost for Regression: Improving the Prediction of Extreme Values N Moniz, RP Ribeiro, V Cerqueira, N Chawla | 39 | 2018 |
| STUDD: a student–teacher method for unsupervised concept drift detection V Cerqueira, HM Gomes, A Bifet, L Torgo Machine Learning 112 (11), 4351-4378, 2023 | 37 | 2023 |
| Automated imbalanced classification via meta-learning N Moniz, V Cerqueira Expert Systems with Applications 178, 115011, 2021 | 35 | 2021 |
| Dynamic and heterogeneous ensembles for time series forecasting V Cerqueira, L Torgo, M Oliveira, B Pfahringer 2017 IEEE international conference on data science and advanced analytics …, 2017 | 35 | 2017 |
| Combining boosted trees with metafeature engineering for predictive maintenance V Cerqueira, F Pinto, C Sá, C Soares Advances in Intelligent Data Analysis XV: 15th International Symposium, IDA …, 2016 | 34 | 2016 |
| Automated setting of bus schedule coverage using unsupervised machine learning J Khiari, L Moreira-Matias, V Cerqueira, O Cats Advances in Knowledge Discovery and Data Mining: 20th Pacific-Asia …, 2016 | 27 | 2016 |
| On evaluating floating car data quality for knowledge discovery V Cerqueira, L Moreira-Matias, J Khiari, H van Lint IEEE Transactions on Intelligent Transportation Systems 19 (11), 3749-3760, 2018 | 18 | 2018 |
| Early anomaly detection in time series: a hierarchical approach for predicting critical health episodes V Cerqueira, L Torgo, C Soares Machine Learning 112 (11), 4409-4430, 2023 | 15 | 2023 |
| Unsupervised concept drift detection using a student–teacher approach V Cerqueira, HM Gomes, A Bifet Discovery Science: 23rd International Conference, DS 2020, Thessaloniki …, 2020 | 14 | 2020 |
| Method and filter for floating car data sources L Moreira-Matias, V Cerqueira, J Khiari US Patent App. 15/429,201, 2018 | 14 | 2018 |
| Autobagging: Learning to rank bagging workflows with metalearning F Pinto, V Cerqueira, C Soares, J Mendes-Moreira arXiv preprint arXiv:1706.09367, 2017 | 14 | 2017 |
| Model selection for time series forecasting: an empirical analysis of multiple estimators V Cerqueira, L Torgo, C Soares Neural processing letters 55 (7), 10073-10091, 2023 | 9* | 2023 |
Luís TorgoCanada Research Chair, Professor, Faculty of Computer Science, Dalhousie UniversityCorreu electrònic verificat a dal.ca