Vítor Cerqueira

Nuno Moniz

Mach. Learn., July, 2024

Meta-learning and Data Augmentation for Stress Testing Forecasting Models.

[BibT_eX]

[DOI]

CoRR, 2024

Forecasting with Deep Learning: Beyond Average of Average of Average Performance.

[BibT_eX]

[DOI]

Luis Roque

CoRR, 2024

On-the-fly Data Augmentation for Forecasting with Deep Learning.

[BibT_eX]

[DOI]

Moisés Rocha dos Santos

Yassine Baghoussi

CoRR, 2024

Meta-TadGAN: Time Series Anomaly Detection Using TadGAN with Meta-features.

[BibT_eX]

[DOI]

Inês Oliveira e Silva

Proceedings of the Progress in Artificial Intelligence, 2024

Lag Selection for Univariate Time Series Forecasting Using Deep Learning: An Empirical Study.

[BibT_eX]

[DOI]

José Leites

Proceedings of the Progress in Artificial Intelligence, 2024

Time Series Data Augmentation as an Imbalanced Learning Problem.

[BibT_eX]

[DOI]

Proceedings of the Progress in Artificial Intelligence, 2024

2023

Model Selection for Time Series Forecasting An Empirical Analysis of Multiple Estimators.

[BibT_eX]

[DOI]

Neural Process. Lett., December, 2023

Early anomaly detection in time series: a hierarchical approach for predicting critical health episodes.

[BibT_eX]

[DOI]

Mach. Learn., November, 2023

STUDD: a student-teacher method for unsupervised concept drift detection.

[BibT_eX]

[DOI]

Mach. Learn., November, 2023

Automated imbalanced classification via layered learning.

[BibT_eX]

[DOI]

Mach. Learn., June, 2023

Towards time-evolving analytics: Online learning for time-dependent evolving data streams.

[BibT_eX]

[DOI]

Data Sci., 2023

Multi-output Ensembles for Multi-step Forecasting.

[BibT_eX]

[DOI]

CoRR, 2023

2022

A case study comparing machine learning with statistical methods for time series forecasting: size matters.

[BibT_eX]

[DOI]

J. Intell. Inf. Syst., 2022

Exceedance Probability Forecasting via Regression for Significant Wave Height Forecasting.

[BibT_eX]

[DOI]

CoRR, 2022

2021

Automated imbalanced classification via meta-learning.

[BibT_eX]

[DOI]

Nuno Moniz

Expert Syst. Appl., 2021

AutoFITS: Automatic Feature Engineering for Irregular Time Series.

[BibT_eX]

[DOI]

Pedro Costa

João Vinagre

CoRR, 2021

Model Compression for Dynamic Forecast Combination.

[BibT_eX]

[DOI]

CoRR, 2021

Model Selection for Time Series Forecasting: Empirical Analysis of Different Estimators.

[BibT_eX]

[DOI]

CoRR, 2021

Empirical Study on the Impact of Different Sets of Parameters of Gradient Boosting Algorithms for Time-Series Forecasting with LightGBM.

[BibT_eX]

[DOI]

Filipa Barros

Proceedings of the PRICAI 2021: Trends in Artificial Intelligence, 2021

2020

Evaluating time series forecasting models: an empirical study on performance estimation methods.

[BibT_eX]

[DOI]

Igor Mozetic

Mach. Learn., 2020

Unsupervised Concept Drift Detection Using a Student-Teacher Approach.

[BibT_eX]

[DOI]

Heitor Murilo Gomes

Albert Bifet

Proceedings of the Discovery Science - 23rd International Conference, 2020

2019

Ensembles for Time Series Forecasting

[BibT_eX]

[DOI]

PhD thesis, 2019

Arbitrage of forecasting experts.

[BibT_eX]

[DOI]

Mach. Learn., 2019

Machine Learning vs Statistical Methods for Time Series Forecasting: Size Matters.

[BibT_eX]

[DOI]

CoRR, 2019

Layered Learning for Early Anomaly Detection: Predicting Critical Health Episodes.

[BibT_eX]

[DOI]

Proceedings of the Discovery Science - 22nd International Conference, 2019

2018

On Evaluating Floating Car Data Quality for Knowledge Discovery.

[BibT_eX]

[DOI]

IEEE Trans. Intell. Transp. Syst., 2018

How to evaluate sentiment classifiers for Twitter time-ordered data?

[BibT_eX]

[DOI]

CoRR, 2018

Constructive Aggregation and Its Application to Forecasting with Dynamic Ensembles.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

SMOTEBoost for Regression: Improving the Prediction of Extreme Values.

[BibT_eX]

[DOI]

Proceedings of the 5th IEEE International Conference on Data Science and Advanced Analytics, 2018

2017

autoBagging: Learning to Rank Bagging Workflows with Metalearning.

[BibT_eX]

[DOI]

Proceedings of the International Workshop on Automatic Selection, 2017

Arbitrated Ensemble for Time Series Forecasting.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Arbitrated Ensemble for Solar Radiation Forecasting.

[BibT_eX]

[DOI]

Proceedings of the Advances in Computational Intelligence, 2017

A Comparative Study of Performance Estimation Methods for Time Series Forecasting.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE International Conference on Data Science and Advanced Analytics, 2017

Dynamic and Heterogeneous Ensembles for Time Series Forecasting.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE International Conference on Data Science and Advanced Analytics, 2017

2016

Automated Setting of Bus Schedule Coverage Using Unsupervised Machine Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2016

CJAMmer - traffic JAM Cause Prediction using Boosted Trees.

[BibT_eX]

[DOI]

Luís Moreira-Matias

Proceedings of the 19th IEEE International Conference on Intelligent Transportation Systems, 2016

Combining Boosted Trees with Metafeature Engineering for Predictive Maintenance.

[BibT_eX]

[DOI]

Proceedings of the Advances in Intelligent Data Analysis XV - 15th International Symposium, 2016

2015

A Framework for Analysing Dynamic Communities in Large-scale Social Networks.

[BibT_eX]

[DOI]