# Open Challenges in Time Series Anomaly Detection: An Industry Perspective

arXiv: https://arxiv.org/abs/2502.05392 · HTML: https://mlsanity.com/paper/2502.05392/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 41 references that are in the map.
- First posted on arXiv 8 Feb 2025.

## Builds on (most cited first, 7 of 7)

- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [Time-Series Anomaly Detection Service at Microsoft](https://mlsanity.com/paper/1906.03821/) (2019)
- [Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark](https://mlsanity.com/paper/1510.03336/) (2015)
- [ClaSP -- Parameter-free Time Series Segmentation](https://mlsanity.com/paper/2207.13987/) (2022)
- [Time Series Anomaly Detection for Smart Grids: A Survey](https://mlsanity.com/paper/2107.08835/) (2021)
- [A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causes](https://mlsanity.com/paper/2206.15033/) (2022)
- [Leveraging Cloud Data to Mitigate User Experience from "Breaking Bad"](https://mlsanity.com/paper/1411.7955/) (2014)

## Built on by (most cited first, 5 of 5)

- [Topology-driven identification of repetitions in multi-variate time series](https://mlsanity.com/paper/2505.10004/) (2025)
- [NetBurst: Event-Centric Forecasting of Bursty, Intermittent Time Series](https://mlsanity.com/paper/2510.22397/) (2025)
- [Exploring Zero-Shot Foundation Models for Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2607.12454/) (2026)
- [Supervision Recovery for Time Series Anomaly Detection via Context-Anchored Pairing](https://mlsanity.com/paper/2609.33610/) (2026)
- [Generalist Representation, Specialist Detection: TS-Router for Time-Series Anomaly Detection](https://mlsanity.com/paper/2610.00978/) (2026)

## Abstract (arXiv)

Current research in time-series anomaly detection is using definitions that miss critical aspects of how anomaly detection is commonly used in practice. We list several areas that are of practical relevance and that we believe are either under-investigated or missing entirely from the current discourse. Based on an investigation of systems deployed in a cloud environment, we motivate the areas of streaming algorithms, human-in-the-loop scenarios, point processes, conditional anomalies and populations analysis of time series. This paper serves as a motivation and call for action, including opportunities for theoretical and applied research, as well as for building new dataset and benchmarks.