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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
  • Nationality

    Portugal
  • Contacts

    +351220402963
    vitor.m.cerqueira@inesctec.pt
001
Publications

2023

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

Authors
Cerqueira, V; Gomes, HM; Bifet, A; Torgo, L;

Publication
Mach. Learn.

Abstract

2023

Automated imbalanced classification via layered learning

Authors
Cerqueira, V; Torgo, L; Branco, P; Bellinger, C;

Publication
Mach. Learn.

Abstract

2023

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

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

Publication
NEURAL PROCESSING LETTERS

Abstract
Evaluating predictive models is a crucial task in predictive analytics. This process is especially challenging with time series data because observations are not independent. Several studies have analyzed how different performance estimation methods compare with each other for approximating the true loss incurred by a given forecasting model. However, these studies do not address how the estimators behave for model selection: the ability to select the best solution among a set of alternatives. This paper addresses this issue. The goal of this work is to compare a set of estimation methods for model selection in time series forecasting tasks. This objective is split into two main questions: (i) analyze how often a given estimation method selects the best possible model; and (ii) analyze what is the performance loss when the best model is not selected. Experiments were carried out using a case study that contains 3111 time series. The accuracy of the estimators for selecting the best solution is low, despite being significantly better than random selection. Moreover, the overall forecasting performance loss associated with the model selection process ranges from 0.28 to 0.58%. Yet, no considerable differences between different approaches were found. Besides, the sample size of the time series is an important factor in the relative performance of the estimators.

2023

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

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

Publication
MACHINE LEARNING

Abstract
The early detection of anomalous events in time series data is essential in many domains of application. In this paper we deal with critical health events, which represent a significant cause of mortality in intensive care units of hospitals. The timely prediction of these events is crucial for mitigating their consequences and improving healthcare. One of the most common approaches to tackle early anomaly detection problems is through standard classification methods. In this paper we propose a novel method that uses a layered learning architecture to address these tasks. One key contribution of our work is the idea of pre-conditional events, which denote arbitrary but computable relaxed versions of the event of interest. We leverage this idea to break the original problem into two hierarchical layers, which we hypothesize are easier to solve. The results suggest that the proposed approach leads to a better performance relative to state of the art approaches for critical health episode prediction.

2022

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

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

Publication
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS

Abstract
Time series forecasting is one of the most active research topics. Machine learning methods have been increasingly adopted to solve these predictive tasks. However, in a recent work, evidence was shown that these approaches systematically present a lower predictive performance relative to simple statistical methods. In this work, we counter these results. We show that these are only valid under an extremely low sample size. Using a learning curve method, our results suggest that machine learning methods improve their relative predictive performance as the sample size grows. The R code to reproduce all of our experiments is available at https://github.com/vcerqueira/MLforForecasting.