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Sobre

Sobre

José Manuel Oliveira tem uma Licenciatura in Matemática Aplicada à Ciência de Computadores em 1992, um Mestrado em Telecomunicações em 1996 e um Doutoramento em Ciências de Engenharia em 2005, todos na Universidade do Porto.

É Professor Auxiliar na Faculdade de Economia da Universidade do Porto, onde integra o agrupamento científico de Matemática e Sistemas de Informação. É investigador no INESC TEC desde 1992, onde desenvolve trabalho de investigação no Centro de Telecomunicações e Multimédia. Os seus interesses de investigação centram-se essencialmente nas Redes sem Fios e incluem a gestão de recursos de rádio, a autoconfiguração de redes e sistemas e o tratamento e análise de dados de comunicações.

José participou em vários projetos de investigação, incluindo os projetos europeus: FP6 DAIDALOS Phase 2, IST VESPER, IST OPIUM e ACTS SCREEN; os projetos QREN: SITMe e Portal Douro; o projeto CMU SELF-PVP; e o projeto P2020 Marecom.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    José Manuel Oliveira
  • Cargo

    Investigador Sénior
  • Desde

    01 dezembro 1992
Publicações

2025

Transformer-Based Models for Probabilistic Time Series Forecasting with Explanatory Variables

Autores
Caetano, R; Oliveira, JM; Ramos, P;

Publicação
MATHEMATICS

Abstract
Accurate demand forecasting is essential for retail operations as it directly impacts supply chain efficiency, inventory management, and financial performance. However, forecasting retail time series presents significant challenges due to their irregular patterns, hierarchical structures, and strong dependence on external factors such as promotions, pricing strategies, and socio-economic conditions. This study evaluates the effectiveness of Transformer-based architectures, specifically Vanilla Transformer, Informer, Autoformer, ETSformer, NSTransformer, and Reformer, for probabilistic time series forecasting in retail. A key focus is the integration of explanatory variables, such as calendar-related indicators, selling prices, and socio-economic factors, which play a crucial role in capturing demand fluctuations. This study assesses how incorporating these variables enhances forecast accuracy, addressing a research gap in the comprehensive evaluation of explanatory variables within multiple Transformer-based models. Empirical results, based on the M5 dataset, show that incorporating explanatory variables generally improves forecasting performance. Models leveraging these variables achieve up to 12.4% reduction in Normalized Root Mean Squared Error (NRMSE) and 2.9% improvement in Mean Absolute Scaled Error (MASE) compared to models that rely solely on past sales. Furthermore, probabilistic forecasting enhances decision making by quantifying uncertainty, providing more reliable demand predictions for risk management. These findings underscore the effectiveness of Transformer-based models in retail forecasting and emphasize the importance of integrating domain-specific explanatory variables to achieve more accurate, context-aware predictions in dynamic retail environments.

2025

Deep Learning-Driven Integration of Multimodal Data for Material Property Predictions

Autores
Costa, V; Oliveira, JM; Ramos, P;

Publicação

Abstract
This study investigates the integration of deep learning for single-modality and multimodal data within materials science. Traditional methods for materials discovery are often resource-intensive and slow, prompting the exploration of machine learning to streamline the prediction of material properties. While single-modality models have been effective, they often miss the complexities inherent in material data. The paper explores multimodal data integration—combining text, images, and tabular data—and demonstrates its potential to improve predictive accuracy. Utilizing the Alexandria dataset, the research introduces a custom methodology involving multimodal data creation, model tuning with AutoGluon framework, and evaluation through targeted fusion techniques. Results reveal that multimodal approaches enhance predictive accuracy and efficiency, particularly when text and image data are integrated. However, challenges remain in predicting complex features like band gaps. Future directions include incorporating new data types and refining specialized models to improve materials discovery and innovation.

2025

Optimizing Credit Risk Prediction for Peer-to-Peer Lending Using Machine Learning

Autores
Souadda, LI; Halitim, AR; Benilles, B; Oliveira, JM; Ramos, P;

Publicação

Abstract
This study investigates the effectiveness of different hyperparameter tuning strategies for peer-to-peer risk management. Ensemble learning techniques have shown superior performance in this field compared to individual classifiers and traditional statistical methods. However, model performance is influenced not only by the choice of algorithm but also by hyperparameter tuning, which impacts both predictive accuracy and computational efficiency. This research compares the performance and efficiency of three widely used hyperparameter tuning methods, Grid Search, Random Search, and Optuna, across XGBoost, LightGBM, and Logistic Regression models. The analysis uses the Lending Club dataset, spanning from 2007 Q1 to 2020 Q3, with comprehensive data preprocessing to address missing values, class imbalance, and feature engineering. Model explainability is assessed through feature importance analysis to identify key drivers of default probability. The findings reveal comparable predictive performance among the tuning methods, evaluated using metrics such as G-mean, sensitivity, and specificity. However, Optuna significantly outperforms the others in computational efficiency; for instance, it is 10.7 times faster than Grid Search for XGBoost and 40.5 times faster for LightGBM. Additionally, variations in feature importance rankings across tuning methods influence model interpretability and the prioritization of risk factors. These insights underscore the importance of selecting appropriate hyperparameter tuning strategies to optimize both performance and explainability in peer-to-peer risk management models.

2024

Evaluating the Effectiveness of Time Series Transformers for Demand Forecasting in Retail

Autores
Oliveira, JM; Ramos, P;

Publicação
MATHEMATICS

Abstract
This study investigates the effectiveness of Transformer-based models for retail demand forecasting. We evaluated vanilla Transformer, Informer, Autoformer, PatchTST, and temporal fusion Transformer (TFT) against traditional baselines like AutoARIMA and AutoETS. Model performance was assessed using mean absolute scaled error (MASE) and weighted quantile loss (WQL). The M5 competition dataset, comprising 30,490 time series from 10 stores, served as the evaluation benchmark. The results demonstrate that Transformer-based models significantly outperform traditional baselines, with Transformer, Informer, and TFT leading the performance metrics. These models achieved MASE improvements of 26% to 29% and WQL reductions of up to 34% compared to the seasonal Na & iuml;ve method, particularly excelling in short-term forecasts. While Autoformer and PatchTST also surpassed traditional methods, their performance was slightly lower, indicating the potential for further tuning. Additionally, this study highlights a trade-off between model complexity and computational efficiency, with Transformer models, though computationally intensive, offering superior forecasting accuracy compared to the significantly slower traditional models like AutoARIMA. These findings underscore the potential of Transformer-based approaches for enhancing retail demand forecasting, provided the computational demands are managed effectively.

2024

Enhancing Hierarchical Sales Forecasting with Promotional Data: A Comparative Study Using ARIMA and Deep Neural Networks

Autores
Teixeira, M; Oliveira, JM; Ramos, P;

Publicação
MACHINE LEARNING AND KNOWLEDGE EXTRACTION

Abstract
Retailers depend on accurate sales forecasts to effectively plan operations and manage supply chains. These forecasts are needed across various levels of aggregation, making hierarchical forecasting methods essential for the retail industry. As competition intensifies, the use of promotions has become a widespread strategy, significantly impacting consumer purchasing behavior. This study seeks to improve forecast accuracy by incorporating promotional data into hierarchical forecasting models. Using a sales dataset from a major Portuguese retailer, base forecasts are generated for different hierarchical levels using ARIMA models and Multi-Layer Perceptron (MLP) neural networks. Reconciliation methods including bottom-up, top-down, and optimal reconciliation with OLS and WLS (struct) estimators are employed. The results show that MLPs outperform ARIMA models for forecast horizons longer than one day. While the addition of regressors enhances ARIMA's accuracy, it does not yield similar improvements for MLP. MLPs present a compelling balance of simplicity and efficiency, outperforming ARIMA in flexibility while offering faster training times and lower computational demands compared to more complex deep learning models, making them highly suitable for practical retail forecasting applications.

Teses
supervisionadas

2022

Previsão de Vendas na Cadeia de Abastecimento no Setor do Retalho Integrando Atividade Promocional

Autor
Mariana Cardoso Teixeira

Instituição
UP-FEP

2017

Wi-Fi Long Distance Maritime Communications Data Analytics

Autor
José Eduardo da Silva Timóteo de Carvalho

Instituição
UP-FEP

2017

Previsão hierárquica de vendas no setor do retalho

Autor
Filipa Sá Couto de Oliveira Fernandes

Instituição
UP-FEP

2017

Impacte de informação promocional na previsão de procura intermitente no setor do retalho

Autor
Marta Filipa Martins Ramos

Instituição
UP-FEP

2014

A Scalable, Self-organizing Communications System for very large Wireless Sensor Networks

Autor
Mohammad Mahmoud Ahmed Abdellatif

Instituição
UP-FEP