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Vítor Cerqueira
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2020 – today
- 2025
- [c22]Luis Roque, Vítor Cerqueira, Carlos Soares, Luís Torgo:
Cherry-Picking in Time Series Forecasting: How to Select Datasets to Make Your Model Shine. AAAI 2025: 20192-20199 - [c21]Flávia Carvalhido
, Henrique Lopes Cardoso, Vítor Cerqueira:
Stress-Testing of Multimodal Models in Medical Image-Based Report Generation. AAAI 2025: 29251-29252 - [c20]Manuel F. Silva, André Dias, Pedro Guedes, Ramiro S. Barbosa, Jorge Estrela, André Moura, Vítor Cerqueira:
An Educational Robotics Competition - The Robotics@ISEP Open Experience. ICARSC 2025: 185-191 - [c19]Ricardo Inácio, Vítor Cerqueira, Marília Barandas, Carlos Soares:
Meta-learning and Data Augmentation for Stress Testing Forecasting Models. IDA 2025: 343-357 - [i21]Heitor Murilo Gomes
CapyMOA: Efficient Machine Learning for Data Streams in Python. CoRR abs/2502.07432 (2025) - [i20]Vítor Cerqueira, Luis Roque, Carlos Soares:
ModelRadar: Aspect-based Forecast Evaluation. CoRR abs/2504.00059 (2025) - 2024
- [j11]Vítor Cerqueira
VEST: automatic feature engineering for forecasting. Mach. Learn. 113(7): 4523-4545 (2024) - [c18]Vítor Cerqueira
Forecasting with Deep Learning: Beyond Average of Average of Average Performance. DS (1) 2024: 135-149 - [c17]José Leites, Vítor Cerqueira, Carlos Soares:
Lag Selection for Univariate Time Series Forecasting Using Deep Learning: An Empirical Study. EPIA (3) 2024: 321-332 - [c16]Vítor Cerqueira
Time Series Data Augmentation as an Imbalanced Learning Problem. EPIA (2) 2024: 335-346 - [c15]Inês Oliveira e Silva, Carlos Soares, Vítor Cerqueira, Arlete Rodrigues, Pedro Bastardo:
Meta-TadGAN: Time Series Anomaly Detection Using TadGAN with Meta-features. EPIA (3) 2024: 347-358 - [i19]Vítor Cerqueira, Moisés Rocha dos Santos, Yassine Baghoussi, Carlos Soares:
On-the-fly Data Augmentation for Forecasting with Deep Learning. CoRR abs/2404.16918 (2024) - [i18]Vítor Cerqueira, Nuno Moniz, Ricardo Inácio, Carlos Soares:
Time Series Data Augmentation as an Imbalanced Learning Problem. CoRR abs/2404.18537 (2024) - [i17]José Leites, Vítor Cerqueira, Carlos Soares:
Lag Selection for Univariate Time Series Forecasting using Deep Learning: An Empirical Study. CoRR abs/2405.11237 (2024) - [i16]Vítor Cerqueira, Luis Roque
Forecasting with Deep Learning: Beyond Average of Average of Average Performance. CoRR abs/2406.16590 (2024) - [i15]Ricardo Inácio
Meta-learning and Data Augmentation for Stress Testing Forecasting Models. CoRR abs/2406.17008 (2024) - [i14]Luis Roque, Carlos Soares, Vítor Cerqueira, Luís Torgo:
Cherry-Picking in Time Series Forecasting: How to Select Datasets to Make Your Model Shine. CoRR abs/2412.14435 (2024) - 2023
- [j10]Giacomo Ziffer
Towards time-evolving analytics: Online learning for time-dependent evolving data streams. Data Sci. 6(1-2): 1-16 (2023) - [j9]Vítor Cerqueira
Automated imbalanced classification via layered learning. Mach. Learn. 112(6): 2083-2104 (2023) - [j8]Vítor Cerqueira
STUDD: a student-teacher method for unsupervised concept drift detection. Mach. Learn. 112(11): 4351-4378 (2023) - [j7]Vítor Cerqueira
Early anomaly detection in time series: a hierarchical approach for predicting critical health episodes. Mach. Learn. 112(11): 4409-4430 (2023) - [j6]Vítor Cerqueira
Model Selection for Time Series Forecasting An Empirical Analysis of Multiple Estimators. Neural Process. Lett. 55(7): 10073-10091 (2023) - [i13]Vítor Cerqueira, Luís Torgo:
Multi-output Ensembles for Multi-step Forecasting. CoRR abs/2306.14563 (2023) - 2022
- [j5]Vítor Cerqueira
A case study comparing machine learning with statistical methods for time series forecasting: size matters. J. Intell. Inf. Syst. 59(2): 415-433 (2022) - [i12]Vítor Cerqueira, Luís Torgo, Paula Branco, Colin Bellinger:
Automated Imbalanced Classification via Layered Learning. CoRR abs/2205.02553 (2022) - [i11]Vítor Cerqueira, Luís Torgo:
Exceedance Probability Forecasting via Regression for Significant Wave Height Forecasting. CoRR abs/2206.09821 (2022) - 2021
- [j4]Nuno Moniz
Automated imbalanced classification via meta-learning. Expert Syst. Appl. 178: 115011 (2021) - [c14]Filipa Barros, Vítor Cerqueira
Empirical Study on the Impact of Different Sets of Parameters of Gradient Boosting Algorithms for Time-Series Forecasting with LightGBM. PRICAI (1) 2021: 454-465 - [i10]Vítor Cerqueira, Heitor Murilo Gomes
STUDD: A Student-Teacher Method for Unsupervised Concept Drift Detection. CoRR abs/2103.00903 (2021) - [i9]Vítor Cerqueira, Luís Torgo, Carlos Soares:
Model Selection for Time Series Forecasting: Empirical Analysis of Different Estimators. CoRR abs/2104.00584 (2021) - [i8]Vítor Cerqueira, Luís Torgo, Carlos Soares, Albert Bifet:
Model Compression for Dynamic Forecast Combination. CoRR abs/2104.01830 (2021) - [i7]Pedro Costa, Vítor Cerqueira, João Vinagre:
AutoFITS: Automatic Feature Engineering for Irregular Time Series. CoRR abs/2112.14806 (2021) - 2020
- [j3]Vítor Cerqueira
Evaluating time series forecasting models: an empirical study on performance estimation methods. Mach. Learn. 109(11): 1997-2028 (2020) - [c13]Vítor Cerqueira
Unsupervised Concept Drift Detection Using a Student-Teacher Approach. DS 2020: 190-204 - [i6]Vítor Cerqueira, Nuno Moniz, Carlos Soares:
VEST: Automatic Feature Engineering for Forecasting. CoRR abs/2010.07137 (2020) - [i5]Vítor Cerqueira, Luís Torgo, Carlos Soares:
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health Episodes. CoRR abs/2010.11595 (2020)
2010 – 2019
- 2019
- [b1]Vítor Cerqueira:
Ensembles for Time Series Forecasting. University of Porto, Portugal, 2019 - [j2]Vítor Cerqueira
Arbitrage of forecasting experts. Mach. Learn. 108(6): 913-944 (2019) - [c12]Vítor Cerqueira
Layered Learning for Early Anomaly Detection: Predicting Critical Health Episodes. DS 2019: 445-459 - [i4]Vítor Cerqueira
Evaluating time series forecasting models: An empirical study on performance estimation methods. CoRR abs/1905.11744 (2019) - [i3]Vítor Cerqueira
Machine Learning vs Statistical Methods for Time Series Forecasting: Size Matters. CoRR abs/1909.13316 (2019) - 2018
- [j1]Vítor Cerqueira
On Evaluating Floating Car Data Quality for Knowledge Discovery. IEEE Trans. Intell. Transp. Syst. 19(11): 3749-3760 (2018) - [c11]Nuno Moniz
SMOTEBoost for Regression: Improving the Prediction of Extreme Values. DSAA 2018: 150-159 - [c10]Vítor Cerqueira
Constructive Aggregation and Its Application to Forecasting with Dynamic Ensembles. ECML/PKDD (1) 2018: 620-636 - [i2]Igor Mozetic, Luís Torgo
How to evaluate sentiment classifiers for Twitter time-ordered data? CoRR abs/1803.05160 (2018) - 2017
- [c9]Vítor Cerqueira
Dynamic and Heterogeneous Ensembles for Time Series Forecasting. DSAA 2017: 242-251 - [c8]Vítor Cerqueira
A Comparative Study of Performance Estimation Methods for Time Series Forecasting. DSAA 2017: 529-538 - [c7]Vítor Cerqueira
Arbitrated Ensemble for Solar Radiation Forecasting. IWANN (1) 2017: 720-732 - [c6]Fábio Pinto, Vítor Cerqueira, Carlos Soares, João Mendes-Moreira:
autoBagging: Learning to Rank Bagging Workflows with Metalearning. AutoML@PKDD/ECML 2017: 21-27 - [c5]Vítor Cerqueira
Arbitrated Ensemble for Time Series Forecasting. ECML/PKDD (2) 2017: 478-494 - [i1]Fábio Pinto, Vítor Cerqueira, Carlos Soares
autoBagging: Learning to Rank Bagging Workflows with Metalearning. CoRR abs/1706.09367 (2017) - 2016
- [c4]Vítor Cerqueira
Combining Boosted Trees with Metafeature Engineering for Predictive Maintenance. IDA 2016: 393-397 - [c3]Luís Moreira-Matias, Vítor Cerqueira
CJAMmer - traffic JAM Cause Prediction using Boosted Trees. ITSC 2016: 743-748 - [c2]Jihed Khiari
Automated Setting of Bus Schedule Coverage Using Unsupervised Machine Learning. PAKDD (1) 2016: 552-564 - 2015
- [c1]Vítor Cerqueira
A Framework for Analysing Dynamic Communities in Large-scale Social Networks. ICEIS (1) 2015: 235-242
Coauthor Index
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last updated on 2025-05-22 00:41 CEST by the dblp team
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