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Wesley M. Gifford

3 accepted papers

2026

Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring.

ICLR 2026poster

We propose a post-hoc adaptive conformal anomaly detection method for monitoring time series that leverages predictions from pre-trained foundation models without requiring additional fine-tuning. Our method yields an interpretable anomaly score directly interpretable as a false alarm rate (p-value)…

Cited by 0SourcecodeScholar
2026

TSPulse: Tiny Pre-Trained Models with Disentangled Representations for Rapid Time-Series Analysis

ICLR 2026poster

Different time-series tasks benefit from distinct cues at various spaces and abstractions, yet existing time-series pre-trained models entangle these signals within large, monolithic embeddings, limiting transferability and zero-shot usability. Moreover, massive model sizes demand heavy compute, res…

Cited by 0SourcecodeScholar
2024

Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series

NeurIPS 2024poster

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 characteristics. Consequently, recent research efforts have focused on developing pre-trained TS forecasting models. These model…