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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
  • Cluster

    Computer Science
  • Role

    External Research Collaborator
  • Since

    23rd June 2014
002
Publications

2022

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

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

Publication
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS

Abstract

2020

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

Authors
Cerqueira, V; Torgo, L; Mozetic, I;

Publication
MACHINE LEARNING

Abstract

2019

Constructive Aggregation and Its Application to Forecasting with Dynamic Ensembles

Authors
Cerqueira, V; Pinto, F; Torgo, L; Soares, C; Moniz, N;

Publication
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2018, PT I

Abstract

2019

Arbitrage of forecasting experts

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

Publication
MACHINE LEARNING

Abstract
Forecasting is an important task across several domains. Its generalised interest is related to the uncertainty and complex evolving structure of time series. Forecasting methods are typically designed to cope with temporal dependencies among observations, but it is widely accepted that none is universally applicable. Therefore, a common solution to these tasks is to combine the opinion of a diverse set of forecasts. In this paper we present an approach based on arbitrating, in which several forecasting models are dynamically combined to obtain predictions. Arbitrating is a metalearning approach that combines the output of experts according to predictions of the loss that they will incur. We present an approach for retrieving out-of-bag predictions that significantly improves its data efficiency. Finally, since diversity is a fundamental component in ensemble methods, we propose a method for explicitly handling the inter-dependence between experts when aggregating their predictions. Results from extensive empirical experiments provide evidence of the method’s competitiveness relative to state of the art approaches. The proposed method is publicly available in a software package. © 2018, The Author(s).

2019

Layered Learning for Early Anomaly Detection: Predicting Critical Health Episodes

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

Publication
Discovery Science - 22nd International Conference, DS 2019, Split, Croatia, October 28-30, 2019, Proceedings

Abstract
Critical health events represent a relevant cause of mortality in intensive care units of hospitals, and their timely prediction has been gaining increasing attention. This problem is an instance of the more general predictive task of early anomaly detection in time series data. One of the most common approaches to solve this problem is to use standard classification methods. In this paper we propose a novel method that uses a layered learning architecture to solve early anomaly detection problems. 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 layers, which we hypothesize are easier to solve. Focusing on critical health episodes, the results suggest that the proposed approach is advantageous relative to state of the art approaches for early anomaly detection. Although we focus on a particular case study, the proposed method is generalizable to other domains. © Springer Nature Switzerland AG 2019.