5 papers
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
Anaísa 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…
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…
Random multiplication versus random sum: auto-regressive-like models with integer-valued random inputs
Abdelhakim Aknouche, Sonia Gouveia, Manuel Scotto
A common approach to analyze count time series is to fit models based on random sum operators. As an alternative, this paper introduces time series models based on a random multipl…