11 citations · 12 across the 4 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…
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…
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.…
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…