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About

About

I am a PhD student at the MAPi Doctoral Programme in Computer Science, hosted at Faculty of Sciences, University of Porto (FCUP) and the Laboratory of Artificial Intelligence and Decision Support (LIAAD – INESC TEC). I finished my BSc in Physics, in 2013, before I went on to get a MSc in Computer Science at the Faculty of Sciences, University of Porto, in 2015.

My research currently focuses on predictive analytics for dependent data, supported financially by an FCT-MAPi grant. My advisors are Prof. Luís Torgo and Prof. Vítor Santos Costa.

Interest
Topics
Details

Details

  • Name

    Mariana Rafaela Oliveira
  • Cluster

    Computer Science
  • Role

    External Student
  • Since

    01st February 2014
Publications

2021

Evaluation Procedures for Forecasting with Spatiotemporal Data

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

Publication
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

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

Publication
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

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

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

Abstract

2017

Dynamic and Heterogeneous Ensembles for Time Series Forecasting

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

Publication
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

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

Publication
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.