# Time Series Foundational Models: Their Role in Anomaly Detection and Prediction

arXiv: https://arxiv.org/abs/2412.19286 · HTML: https://mlsanity.com/paper/2412.19286/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 26 Dec 2024.

## Builds on (most cited first, 13 of 13)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://mlsanity.com/paper/2210.02186/) (2022)
- [Chronos: Learning the Language of Time Series](https://mlsanity.com/paper/2403.07815/) (2024)
- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://mlsanity.com/paper/2310.01728/) (2023)
- [Unified Training of Universal Time Series Forecasting Transformers](https://mlsanity.com/paper/2402.02592/) (2024)
- [Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts](https://mlsanity.com/paper/2409.16040/) (2024)
- [TimeGPT-1](https://mlsanity.com/paper/2310.03589/) (2023)
- [Large Language Models are Few-Shot Health Learners](https://mlsanity.com/paper/2305.15525/) (2023)
- [Large Language Models for Time Series: A Survey](https://mlsanity.com/paper/2402.01801/) (2024)
- [Large Language Models for Spatial Trajectory Patterns Mining](https://mlsanity.com/paper/2310.04942/) (2023)
- [Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform](https://mlsanity.com/paper/2401.15544/) (2024)
- [Dataset: Rare Event Classification in Multivariate Time Series](https://mlsanity.com/paper/1809.10717/) (2018)

## Built on by (most cited first, 4 of 4)

- [Time-Prompt: Integrated Heterogeneous Prompts for Unlocking LLMs in Time Series Forecasting](https://mlsanity.com/paper/2506.17631/) (2025)
- [SmartPilot: A Multiagent CoPilot for Adaptive and Intelligent Manufacturing](https://mlsanity.com/paper/2505.06492/) (2025)
- [THEMIS: Unlocking Pretrained Knowledge with Foundation Model Embeddings for Anomaly Detection in Time Series](https://mlsanity.com/paper/2510.03911/) (2025)
- [A Comparative Study of Adaptation Strategies for Time Series Foundation Models in Anomaly Detection](https://mlsanity.com/paper/2601.00446/) (2026)

## Abstract (arXiv)

Time series foundational models (TSFM) have gained prominence in time series forecasting, promising state-of-the-art performance across various applications. However, their application in anomaly detection and prediction remains underexplored, with growing concerns regarding their black-box nature, lack of interpretability and applicability. This paper critically evaluates the efficacy of TSFM in anomaly detection and prediction tasks. We systematically analyze TSFM across multiple datasets, including those characterized by the absence of discernible patterns, trends and seasonality. Our analysis shows that while TSFMs can be extended for anomaly detection and prediction, traditional statistical and deep learning models often match or outperform TSFM in these tasks. Additionally, TSFMs require high computational resources but fail to capture sequential dependencies effectively or improve performance in few-shot or zero-shot scenarios. noindent The preprocessed datasets, codes to reproduce the results and supplementary materials are available at https://github.com/smtmnfg/TSFM.