# Semantic Discord: Finding Unusual Local Patterns for Time Series

arXiv: https://arxiv.org/abs/2001.11842 · HTML: https://mlsanity.com/paper/2001.11842/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2020.

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

- [Robust Time Series Chain Discovery with Incremental Nearest Neighbors](https://mlsanity.com/paper/2211.02146/) (2022)
- [QABBA: Symbolic Time-Series Compression via Integer-Quantized Aggregation](https://mlsanity.com/paper/2411.15209/) (2024)
- [Adaptive von Mises-Fisher Likelihood Loss for Supervised Deep Time Series Hashing](https://mlsanity.com/paper/2509.19625/) (2025)
- [An Unsupervised Time Series Anomaly Detection Approach for Efficient Online Process Monitoring of Additive Manufacturing](https://mlsanity.com/paper/2510.09977/) (2025)
- [Joint Time Series Chain: Detecting Unusual Evolving Trend across Time Series](https://mlsanity.com/paper/2602.13649/) (2026)

## Abstract (arXiv)

Finding anomalous subsequence in a long time series is a very important but difficult problem. Existing state-of-the-art methods have been focusing on searching for the subsequence that is the most dissimilar to the rest of the subsequences; however, they do not take into account the background patterns that contain the anomalous candidates. As a result, such approaches are likely to miss local anomalies. We introduce a new definition named textitsemantic discord, which incorporates the context information from larger subsequences containing the anomaly candidates. We propose an efficient algorithm with a derived lower bound that is up to 3 orders of magnitude faster than the brute force algorithm in real world data. We demonstrate that our method significantly outperforms the state-of-the-art methods in locating anomalies by extensive experiments. We further explain the interpretability of semantic discord.