4 papers
Revisiting OmniAnomaly for Anomaly Detection: performance metrics and comparison with PCA-based models
Bruna Alves, Ana Martins, Armando J. Pinho +1
Deep learning models have become the dominant approach for multivariate time series anomaly detection (MTSAD), often reporting substantial performance improvements over classical s…
Unified Taxonomy for Multivariate Time Series Anomaly Detection using Deep Learning
Bruna Alves, Armando J. Pinho, Sónia Gouveia
The topic of Multivariate Time Series Anomaly Detection (MTSAD) has grown rapidly over the past years, with a steady rise in publications and Deep Learning (DL) models becoming the…
A two-step sequential approach for hyperparameter selection in finite context models
José Contente, Ana Martins, Armando J. Pinho +1
Finite-context models (FCMs) are widely used for compressing symbolic sequences such as DNA, where predictive performance depends critically on the context length k and smoothing p…
Fast and Interpretable Autoregressive Estimation with Neural Network Backpropagation
AnaÃsa Lucena, Ana Martins, Armando J. Pinho +1
Autoregressive (AR) models remain widely used in time series analysis due to their interpretability, but convencional parameter estimation methods can be computationally expensive…