# TSFM in-context learning for time-series classification of bearing-health status

arXiv: https://arxiv.org/abs/2511.15447 · HTML: https://mlsanity.com/paper/2511.15447/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 19 Nov 2025.

## Builds on (most cited first, 5 of 5)

- [A decoder-only foundation model for time-series forecasting](https://mlsanity.com/paper/2310.10688/) (2023)
- [Unified Training of Universal Time Series Forecasting Transformers](https://mlsanity.com/paper/2402.02592/) (2024)
- [Foundation Models for Time Series Analysis: A Tutorial and Survey](https://mlsanity.com/paper/2403.14735/) (2024)
- [Chronos-2: From Univariate to Universal Forecasting](https://mlsanity.com/paper/2510.15821/) (2025)
- [Pre-trained Forecasting Models: Strong Zero-Shot Feature Extractors for Time Series Classification](https://mlsanity.com/paper/2510.26777/) (2025)

## Abstract (arXiv)

We introduce a classification method based on in-context learning using time-series foundation models (TSFMs). We demonstrate how data not included in the TSFM training can be classified without fine-tuning the foundation model or training a traditional classification model. Examples are represented as targets (class labels) and covariates (data matrices) within the TSFM prompt, enabling the classification of unknown covariate data patterns alongside the forecast horizon through in-context learning. We apply this method to vibration data to assess the health state of a bearing within a servo-press motor. The method transforms frequency-domain reference signals into pseudo time-series patterns, generates aligned covariate and target signals, and uses the TSFM to predict class-membership probabilities for predefined labels. Leveraging the scalability of pre-trained models, the proposed method demonstrates effectiveness across varying operational conditions. This represents significant progress beyond traditional, custom AI solutions towards broader AI-driven maintenance systems that could potentially be provided as Model- or Software-as-a-Service applications.