# MTV: Visual Analytics for Detecting, Investigating, and Annotating Anomalies in Multivariate Time Series

arXiv: https://arxiv.org/abs/2112.05734 · HTML: https://mlsanity.com/paper/2112.05734/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2021.

## Built on by (most cited first, 11 of 11)

- [Sintel: A Machine Learning Framework to Extract Insights from Signals](https://mlsanity.com/paper/2204.09108/) (2022)
- [DeepVATS: Deep Visual Analytics for Time Series](https://mlsanity.com/paper/2302.03858/) (2023)
- [The Need for Interpretable Features: Motivation and Taxonomy](https://mlsanity.com/paper/2202.11748/) (2022)
- [A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series](https://mlsanity.com/paper/2405.03234/) (2024)
- [From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management](https://mlsanity.com/paper/2304.03368/) (2023)
- [Generative Pre-Training of Time-Series Data for Unsupervised Fault Detection in Semiconductor Manufacturing](https://mlsanity.com/paper/2309.11427/) (2023)
- [SigTime: Learning and Visually Explaining Time Series Signatures](https://mlsanity.com/paper/2512.12076/) (2025)
- [Trust Calibration as a Function of the Evolution of Uncertainty in Knowledge Generation: A Survey](https://mlsanity.com/paper/2209.04388/) (2022)
- [Towards Meaningful Anomaly Detection: The Effect of Counterfactual Explanations on the Investigation of Anomalies in Multivariate Time Series](https://mlsanity.com/paper/2302.03302/) (2023)
- ["Show Me What's Wrong!": Combining Charts and Text to Guide Data Analysis](https://mlsanity.com/paper/2410.00727/) (2024)
- [ConceptTS: LLM-Guided Concept Bottlenecks for Interpretable Multivariate Time-Series Forecasting](https://mlsanity.com/paper/2608.21277/) (2026)

## Abstract (arXiv)

Detecting anomalies in time-varying multivariate data is crucial in various industries for the predictive maintenance of equipment. Numerous machine learning (ML) algorithms have been proposed to support automated anomaly identification. However, a significant amount of human knowledge is still required to interpret, analyze, and calibrate the results of automated analysis. This paper investigates current practices used to detect and investigate anomalies in time series data in industrial contexts and identifies corresponding needs. Through iterative design and working with nine experts from two industry domains (aerospace and energy), we characterize six design elements required for a successful visualization system that supports effective detection, investigation, and annotation of time series anomalies. We summarize an ideal human-AI collaboration workflow that streamlines the process and supports efficient and collaborative analysis. We introduce MTV (Multivariate Time Series Visualization), a visual analytics system to support such workflow. The system incorporates a set of novel visualization and interaction designs to support multi-faceted time series exploration, efficient in-situ anomaly annotation, and insight communication. Two user studies, one with 6 spacecraft experts (with routine anomaly analysis tasks) and one with 25 general end-users (without such tasks), are conducted to demonstrate the effectiveness and usefulness of MTV.