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About

About

Vitor Cerqueira received his Licenciate degree on Applied Mathematics and MSc on Data Analytics from the Faculty of Sciences, U. Porto, in 2012 and from the Faculty of Economics, also U. Porto, in 2014, respectively. Currently, he is pursuing his Ph.D degree in the doctoral program for Informatics Engineering from the University of Porto.

He is a research fellow in LIAAD, a laboratory for Artificial Intelligence and Decision Support Systems. His main research topic is related to ensemble learning for time series forecasting tasks and actionable forecasting methods. 

Interest
Topics
Details

Details

  • Name

    Vítor Manuel Cerqueira
  • Role

    External Research Collaborator
  • Since

    23rd June 2014
001
Publications

2025

Meta-learning and Data Augmentation for Stress Testing Forecasting Models

Authors
Inácio, R; Cerqueira, V; Barandas, M; Soares, C;

Publication
Advances in Intelligent Data Analysis XXIII - 23rd International Symposium on Intelligent Data Analysis, IDA 2025, Konstanz, Germany, May 7-9, 2025, Proceedings

Abstract
The effectiveness of time series forecasting models can be hampered by conditions in the input space that lead them to underperform. When those are met, negative behaviours, such as higher-than-usual errors or increased uncertainty are shown. Traditionally, stress testing is applied to assess how models respond to adverse, but plausible scenarios, providing insights on how to improve their robustness and reliability. This paper builds upon this technique by contributing with a novel framework called MAST (Meta-learning and data Augmentation for Stress Testing). In particular, MAST is a meta-learning approach that predicts the probability that a given model will perform poorly on a given time series based on a set of statistical features. This way, instead of designing new stress scenarios, this method uses the information provided by instances that led to decreases in forecasting performance. An additional contribution is made, a novel time series data augmentation technique based on oversampling, that improves the information about stress factors in the input space, which elevates the classification capabilities of the method. We conducted experiments using 6 benchmark datasets containing a total of 97.829 time series. The results suggest that MAST is able to identify conditions that lead to large errors effectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

2025

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

Authors
Cerqueira, V; Roque, L; Soares, C;

Publication
DISCOVERY SCIENCE, DS 2024, PT I

Abstract
Accurate evaluation of forecasting models is essential for ensuring reliable predictions. Current practices for evaluating and comparing forecasting models focus on summarising performance into a single score, using metrics such as SMAPE. We hypothesize that averaging performance over all samples dilutes relevant information about the relative performance of models. Particularly, conditions in which this relative performance is different than the overall accuracy. We address this limitation by proposing a novel framework for evaluating univariate time series forecasting models from multiple perspectives, such as one-step ahead forecasting versus multi-step ahead forecasting. We show the advantages of this framework by comparing a state-of-the-art deep learning approach with classical forecasting techniques. While classical methods (e.g. ARIMA) are long-standing approaches to forecasting, deep neural networks (e.g. NHITS) have recently shown state-of-the-art forecasting performance in benchmark datasets. We conducted extensive experiments that show NHITS generally performs best, but its superiority varies with forecasting conditions. For instance, concerning the forecasting horizon, NHITS only outperforms classical approaches for multi-step ahead forecasting. Another relevant insight is that, when dealing with anomalies, NHITS is outperformed by methods such as Theta. These findings highlight the importance of evaluating forecasts from multiple dimensions.

2025

ModelRadar: Aspect-based Forecast Evaluation

Authors
Cerqueira, V; Roque, L; Soares, C;

Publication
CoRR

Abstract

2025

Stress-Testing of Multimodal Models in Medical Image-Based Report Generation

Authors
Carvalhido, F; Cardoso, HL; Cerqueira, V;

Publication
AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25 - March 4, 2025, Philadelphia, PA, USA

Abstract
Multimodal models, namely vision-language models, present unique possibilities through the seamless integration of different information mediums for data generation. These models mostly act as a black-box, making them lack transparency and explicability. Reliable results require accountable and trustworthy Artificial Intelligence (AI), namely when in use for critical tasks, such as the automatic generation of medical imaging reports for healthcare diagnosis. By exploring stress-testing techniques, multimodal generative models can become more transparent by disclosing their shortcomings, further supporting their responsible usage in the medical field. Copyright © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

2025

CapyMOA: Efficient Machine Learning for Data Streams in Python

Authors
Gomes, HM; Lee, A; Gunasekara, N; Sun, Y; Cassales, GW; Liu, J; Heyden, M; Cerqueira, V; Bahri, M; Koh, YS; Pfahringer, B; Bifet, A;

Publication
CoRR

Abstract