A Framework for Analysing Dynamic Communities in Large-scale Social Networks. - Vítor Cerqueira; Márcia D. B. Oliveira; João Gama 001 (ICEIS (1), 2015)

Combining Boosted Trees with Metafeature Engineering for Predictive Maintenance. - Vítor Cerqueira; Fábio Pinto; Cláudio Rebelo de Sá; Carlos Soares (IDA, 2016)

CJAMmer - traffic JAM Cause Prediction using Boosted Trees. - Luís Moreira-Matias; Vítor Cerqueira (ITSC, 2016)

Automated Setting of Bus Schedule Coverage Using Unsupervised Machine Learning. - Jihed Khiari; Luís Moreira-Matias; Vítor Cerqueira; Oded Cats (PAKDD (1), 2016)

Dynamic and Heterogeneous Ensembles for Time Series Forecasting. - Vítor Cerqueira; Luís Torgo; Mariana Oliveira 001; Bernhard Pfahringer (DSAA, 2017)

A Comparative Study of Performance Estimation Methods for Time Series Forecasting. - Vítor Cerqueira; Luís Torgo; Jasmina Smailovic; Igor Mozetic (DSAA, 2017)

Arbitrated Ensemble for Solar Radiation Forecasting. - Vítor Cerqueira; Luís Torgo; Carlos Soares (IWANN (1), 2017)

Arbitrated Ensemble for Time Series Forecasting. - Vítor Cerqueira; Luís Torgo; Fábio Pinto; Carlos Soares (ECML/PKDD (2), 2017)

autoBagging: Learning to Rank Bagging Workflows with Metalearning. - Fábio Pinto; Vítor Cerqueira; Carlos Soares; João Mendes-Moreira 001 (AutoML@PKDD/ECML, 2017)

SMOTEBoost for Regression: Improving the Prediction of Extreme Values. - Nuno Moniz; Rita P. Ribeiro; Vítor Cerqueira; Nitesh V. Chawla (DSAA, 2018)

Constructive Aggregation and Its Application to Forecasting with Dynamic Ensembles. - Vítor Cerqueira; Fábio Pinto; Luís Torgo; Carlos Soares; Nuno Moniz (ECML/PKDD (1), 2018)

How to evaluate sentiment classifiers for Twitter time-ordered data? - Igor Mozetic; Luís Torgo; Vítor Cerqueira; Jasmina Smailovic (CoRR, 2018)

On Evaluating Floating Car Data Quality for Knowledge Discovery. - Vítor Cerqueira; Luís Moreira-Matias; Jihed Khiari; Hans van Lint (IEEE Trans. Intell. Transp. Syst., 2018)

Layered Learning for Early Anomaly Detection: Predicting Critical Health Episodes. - Vítor Cerqueira; Luís Torgo; Carlos Soares (DS, 2019)

Machine Learning vs Statistical Methods for Time Series Forecasting: Size Matters. - Vítor Cerqueira; Luís Torgo; Carlos Soares (CoRR, 2019)

Arbitrage of forecasting experts. - Vítor Cerqueira; Luís Torgo; Fábio Pinto; Carlos Soares (Mach. Learn., 2019)

Ensembles for Time Series Forecasting - Vítor Cerqueira (2019)

Unsupervised Concept Drift Detection Using a Student-Teacher Approach. - Vítor Cerqueira; Heitor Murilo Gomes; Albert Bifet (DS, 2020)

Evaluating time series forecasting models: an empirical study on performance estimation methods. - Vítor Cerqueira; Luís Torgo; Igor Mozetic (Mach. Learn., 2020)

Empirical Study on the Impact of Different Sets of Parameters of Gradient Boosting Algorithms for Time-Series Forecasting with LightGBM. - Filipa Barros; Vítor Cerqueira; Carlos Soares (PRICAI (1), 2021)

Model Selection for Time Series Forecasting: Empirical Analysis of Different Estimators. - Vítor Cerqueira; Luís Torgo; Carlos Soares (CoRR, 2021)

Model Compression for Dynamic Forecast Combination. - Vítor Cerqueira; Luís Torgo; Carlos Soares; Albert Bifet (CoRR, 2021)

AutoFITS: Automatic Feature Engineering for Irregular Time Series. - Pedro Costa; Vítor Cerqueira; João Vinagre (CoRR, 2021)

Automated imbalanced classification via meta-learning. - Nuno Moniz; Vítor Cerqueira (Expert Syst. Appl., 2021)

Exceedance Probability Forecasting via Regression for Significant Wave Height Forecasting. - Vítor Cerqueira; Luís Torgo (CoRR, 2022)

A case study comparing machine learning with statistical methods for time series forecasting: size matters. - Vítor Cerqueira; Luís Torgo; Carlos Soares (J. Intell. Inf. Syst., 2022)

Multi-output Ensembles for Multi-step Forecasting. - Vítor Cerqueira; Luís Torgo (CoRR, 2023)

Towards time-evolving analytics: Online learning for time-dependent evolving data streams. - Giacomo Ziffer; Alessio Bernardo; Emanuele Della Valle; Vítor Cerqueira; Albert Bifet (Data Sci., 2023)

Automated imbalanced classification via layered learning. - Vítor Cerqueira; Luís Torgo; Paula Branco; Colin Bellinger (Mach. Learn., 2023)

STUDD: a student-teacher method for unsupervised concept drift detection. - Vítor Cerqueira; Heitor Murilo Gomes; Albert Bifet; Luís Torgo (Mach. Learn., 2023)

Early anomaly detection in time series: a hierarchical approach for predicting critical health episodes. - Vítor Cerqueira; Luís Torgo; Carlos Soares (Mach. Learn., 2023)

Model Selection for Time Series Forecasting An Empirical Analysis of Multiple Estimators. - Vítor Cerqueira; Luís Torgo; Carlos Soares (Neural Process. Lett., 2023)

Forecasting with Deep Learning: Beyond Average of Average of Average Performance. - Vítor Cerqueira; Luis Roque; Carlos Soares (DS (1), 2024)

Time Series Data Augmentation as an Imbalanced Learning Problem. - Vítor Cerqueira; Nuno Moniz; Ricardo Inácio; Carlos Soares (EPIA (2), 2024)

Lag Selection for Univariate Time Series Forecasting Using Deep Learning: An Empirical Study. - José Leites; Vítor Cerqueira; Carlos Soares (EPIA (3), 2024)

Meta-TadGAN: Time Series Anomaly Detection Using TadGAN with Meta-features. - Inês Oliveira e Silva; Carlos Soares; Vítor Cerqueira; Arlete Rodrigues; Pedro Bastardo (EPIA (3), 2024)

On-the-fly Data Augmentation for Forecasting with Deep Learning. - Vítor Cerqueira; Moisés Rocha dos Santos; Yassine Baghoussi; Carlos Soares (CoRR, 2024)

VEST: automatic feature engineering for forecasting. - Vítor Cerqueira; Nuno Moniz; Carlos Soares (Mach. Learn., 2024)

Stress-Testing of Multimodal Models in Medical Image-Based Report Generation. - Flávia Carvalhido; Henrique Lopes Cardoso; Vítor Cerqueira (AAAI, 2025)

Cherry-Picking in Time Series Forecasting: How to Select Datasets to Make Your Model Shine. - Luis Roque; Vítor Cerqueira; Carlos Soares; Luís Torgo (AAAI, 2025)

An Educational Robotics Competition - The Robotics@ISEP Open Experience. - Manuel F. Silva 001; André Dias; Pedro Guedes; Ramiro S. Barbosa; Jorge Estrela; André Moura; Vítor Cerqueira (ICARSC, 2025)

Meta-learning and Data Augmentation for Stress Testing Forecasting Models. - Ricardo Inácio; Vítor Cerqueira; Marília Barandas; Carlos Soares (IDA, 2025)

CapyMOA: Efficient Machine Learning for Data Streams in Python. - Heitor Murilo Gomes; Anton Lee; Nuwan Gunasekara; Yibin Sun; Guilherme Weigert Cassales; Justin Liu; Marco Heyden; Vítor Cerqueira; Maroua Bahri; Yun Sing Koh; Bernhard Pfahringer; Albert Bifet (CoRR, 2025)

ModelRadar: Aspect-based Forecast Evaluation. - Vítor Cerqueira; Luis Roque; Carlos Soares (CoRR, 2025)

Simulating Biases for Interpretable Fairness in Offline and Online Classifiers. - Ricardo Inácio; Zafeiris Kokkinogenis; Vítor Cerqueira; Carlos Soares (CoRR, 2025)

L-GTA: Latent Generative Modeling for Time Series Augmentation. - Luis Roque; Carlos Soares; Vítor Cerqueira; Luís Torgo (CoRR, 2025)

N-BEATS-MOE: N-BEATS with a Mixture-of-Experts Layer for Heterogeneous Time Series Forecasting. - Ricardo Matos; Luis Roque; Vítor Cerqueira (CoRR, 2025)