124 citations · 189 across the 15 of their papers we have counts for
6 papers · 1 filter
Interpretable Latent Variables in Deep State Space Models
Haoxuan Wu, David S. Matteson, Martin T. Wells
We introduce a new version of deep state-space models (DSSMs) that combines a recurrent neural network with a state-space framework to forecast time series data. The model estimate…
IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance
Grace Deng, Cuize Han, Tommaso Dreossi +2
Classification of large multivariate time series with strong class imbalance is an important task in real-world applications. Standard methods of class weights, oversampling, or pa…
Copula Quadrant Similarity for Anomaly Scores
Matthew Davidow, David Matteson
Practical anomaly detection requires applying numerous approaches due to the inherent difficulty of unsupervised learning. Direct comparison between complex or opaque anomaly detec…
Graph-Based Continual Learning
Binh Tang, David S. Matteson
Despite significant advances, continual learning models still suffer from catastrophic forgetting when exposed to incrementally available data from non-stationary distributions. Re…
Factor Analysis of Mixed Data for Anomaly Detection
Matthew Davidow, David S. Matteson
Anomaly detection aims to identify observations that deviate from the typical pattern of data. Anomalous observations may correspond to financial fraud, health risks, or incorrectl…
Interpretable Vector AutoRegressions with Exogenous Time Series
Ines Wilms, Sumanta Basu, Jacob Bien +1
The Vector AutoRegressive (VAR) model is fundamental to the study of multivariate time series. Although VAR models are intensively investigated by many researchers, practitioners o…