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Sobre

Sobre

Sou estudante de doutoramento no programa doutoral em Informática MAPi, estando acolhida na Faculdade de Ciências da Universidade do Porto (FCUP) e no Laboratório de Apoio Laboratório de Inteligência Artificial e Apoio à Decisão (LIAAD – INESC TEC). Em 2013, terminei a minha Licenciatura em Física, antes de obter o Mestrado em Ciência de Computadores na Faculdade de Ciências da Universidade do Porto (que completei em 2015).

Os tópicos de investigação que mais me interessam são em Machine Learning / Data Mining, focando-me principalmente em tarefas previsão envolvendo dados com dependências.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Mariana Rafaela Oliveira
  • Cluster

    Informática
  • Cargo

    Estudante Externo
  • Desde

    01 fevereiro 2014
Publicações

2021

Evaluation Procedures for Forecasting with Spatiotemporal Data

Autores
Oliveira, M; Torgo, L; Santos Costa, V;

Publicação
Mathematics

Abstract
The increasing use of sensor networks has led to an ever larger number of available spatiotemporal datasets. Forecasting applications using this type of data are frequently motivated by important domains such as environmental monitoring. Being able to properly assess the performance of different forecasting approaches is fundamental to achieve progress. However, traditional performance estimation procedures, such as cross-validation, face challenges due to the implicit dependence between observations in spatiotemporal datasets. In this paper, we empirically compare several variants of cross-validation (CV) and out-of-sample (OOS) performance estimation procedures, using both artificially generated and real-world spatiotemporal datasets. Our results show both CV and OOS reporting useful estimates, but they suggest that blocking data in space and/or in time may be useful in mitigating CV’s bias to underestimate error. Overall, our study shows the importance of considering data dependencies when estimating the performance of spatiotemporal forecasting models.

2019

Evaluation Procedures for Forecasting with Spatio-Temporal Data

Autores
Oliveira, M; Torgo, L; Costa, VS;

Publicação
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part I

Abstract

2019

Biased Resampling Strategies for Imbalanced Spatio-Temporal Forecasting

Autores
Oliveira, M; Moniz, N; Torgo, L; Costa, VS;

Publicação
2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

Abstract

2017

Dynamic and Heterogeneous Ensembles for Time Series Forecasting

Autores
Cerqueira, V; Torgo, L; Oliveira, M; Pfahringer, B;

Publicação
2017 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2017, Tokyo, Japan, October 19-21, 2017

Abstract

2016

Predicting Wildfires Propositional and Relational Spatio-Temporal Pre-processing Approaches

Autores
Oliveira, M; Torgo, L; Costa, VS;

Publicação
DISCOVERY SCIENCE, (DS 2016)

Abstract
We present and evaluate two different methods for building spatio-temporal features: a propositional method and a method based on propositionalisation of relational clauses. Our motivating application, a regression problem, requires the prediction of the fraction of each Portuguese parish burnt yearly by wildfires - a problem with a strong socio-economic and environmental impact in the country. We evaluate and compare how these methods perform individually and combined together. We successfully use under-sampling to deal with the high skew in the data set. We find that combining the approaches significantly improves the similar results obtained by each method individually.