1 citations · 1 across the 3 of their papers we have counts for
4 papers
Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring
Natalia Martinez Gil, Fearghal O'Donncha, Wesley M. Gifford +3
We propose a post-hoc adaptive conformal anomaly detection method for monitoring time series that leverages predictions from pre-trained foundation models without requiring additio…
TSPulse: Tiny Pre-Trained Models with Disentangled Representations for Rapid Time-Series Analysis
Vijay Ekambaram, Subodh Kumar, Arindam Jati +5
Time-series tasks often benefit from signals expressed across multiple representation spaces (e.g., time vs. frequency) and at varying abstraction levels (e.g., local patterns vs.…
Revisiting the Generic Transformer: Deconstructing a Strong Baseline for Time Series Foundation Models
Yunshi Wen, Wesley M. Gifford, Chandra Reddy +3
The recent surge in Time Series Foundation Models has rapidly advanced the field, yet the heterogeneous training setups across studies make it difficult to attribute improvements t…
Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series
Vijay Ekambaram, Arindam Jati, Pankaj Dayama +5
Large pre-trained models excel in zero/few-shot learning for language and vision tasks but face challenges in multivariate time series (TS) forecasting due to diverse data characte…