# Sketching Multidimensional Time Series for Fast Discord Mining

arXiv: https://arxiv.org/abs/2311.03393 · HTML: https://mlsanity.com/paper/2311.03393/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 58 references that are in the map.
- First posted on arXiv 5 Nov 2023.

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

- [Sketching as a Tool for Numerical Linear Algebra](https://mlsanity.com/paper/1411.4357/) (2014)
- [MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks](https://mlsanity.com/paper/1901.04997/) (2019)
- [Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress](https://mlsanity.com/paper/2009.13807/) (2020)
- [Temporal Pattern Attention for Multivariate Time Series Forecasting](https://mlsanity.com/paper/1809.04206/) (2018)
- [Towards a Rigorous Evaluation of Time-series Anomaly Detection](https://mlsanity.com/paper/2109.05257/) (2021)
- [On Coresets for Logistic Regression](https://mlsanity.com/paper/1805.08571/) (2018)
- [Frequent Directions : Simple and Deterministic Matrix Sketching](https://mlsanity.com/paper/1501.01711/) (2015)
- [Discrepancy, Coresets, and Sketches in Machine Learning](https://mlsanity.com/paper/1906.04845/) (2019)
- [Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph](https://mlsanity.com/paper/2208.05648/) (2022)
- [Error-bounded Approximate Time Series Joins Using Compact Dictionary Representations of Time Series](https://mlsanity.com/paper/2112.12965/) (2021)
- [Time is of the Essence: Machine Learning-based Intrusion Detection in Industrial Time Series Data](https://mlsanity.com/paper/1809.07500/) (2018)
- [Discovering Subdimensional Motifs of Different Lengths in Large-Scale Multivariate Time Series](https://mlsanity.com/paper/1911.09218/) (2019)
- [Online Multi-horizon Transaction Metric Estimation with Multi-modal Learning in Payment Networks](https://mlsanity.com/paper/2109.10020/) (2021)

## Built on by (most cited first, 3 of 3)

- [RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Data](https://mlsanity.com/paper/2402.10487/) (2024)
- [Matrix Profile for Anomaly Detection on Multidimensional Time Series](https://mlsanity.com/paper/2409.09298/) (2024)
- [Robotic Classification of Divers' Swimming States using Visual Pose Keypoints as IMUs](https://mlsanity.com/paper/2510.17863/) (2025)

## Abstract (arXiv)

Time series discords are a useful primitive for time series anomaly detection, and the matrix profile is capable of capturing discord effectively. There exist many research efforts to improve the scalability of discord discovery with respect to the length of time series. However, there is surprisingly little work focused on reducing the time complexity of matrix profile computation associated with dimensionality of a multidimensional time series. In this work, we propose a sketch for discord mining among multi-dimensional time series. After an initial pre-processing of the sketch as fast as reading the data, the discord mining has runtime independent of the dimensionality of the original data. On several real world examples from water treatment and transportation, the proposed algorithm improves the throughput by at least an order of magnitude (50X) and only has minimal impact on the quality of the approximated solution. Additionally, the proposed method can handle the dynamic addition or deletion of dimensions inconsequential overhead. This allows a data analyst to consider "what-if" scenarios in real time while exploring the data.