# ConceptExplorer: Visual Analysis of Concept Driftsin Multi-source Time-series Data

arXiv: https://arxiv.org/abs/2007.15272 · HTML: https://mlsanity.com/paper/2007.15272/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2020.

## Built on by (most cited first, 12 of 12)

- [Visual Analytics For Machine Learning: A Data Perspective Survey](https://mlsanity.com/paper/2307.07712/) (2023)
- [Model Based Explanations of Concept Drift](https://mlsanity.com/paper/2303.09331/) (2023)
- [Unsupervised Concept Drift Detection from Deep Learning Representations in Real-time](https://mlsanity.com/paper/2406.17813/) (2024)
- [One or Two Things We know about Concept Drift -- A Survey on Monitoring Evolving Environments](https://mlsanity.com/paper/2310.15826/) (2023)
- [DeepLens: Interactive Out-of-distribution Data Detection in NLP Models](https://mlsanity.com/paper/2303.01577/) (2023)
- [Contrastive Identification of Covariate Shift in Image Data](https://mlsanity.com/paper/2108.08000/) (2021)
- [Data+Shift: Supporting visual investigation of data distribution shifts by data scientists](https://mlsanity.com/paper/2204.14025/) (2022)
- [Visualization for Trust in Machine Learning Revisited: The State of the Field in 2023](https://mlsanity.com/paper/2403.12005/) (2024)
- [ESCAPE: Countering Systematic Errors from Machine's Blind Spots via Interactive Visual Analysis](https://mlsanity.com/paper/2303.09657/) (2023)
- [Concept Drift Visualization of SVM with Shifting Window](https://mlsanity.com/paper/2406.13754/) (2024)
- [A Comparative Visual Analytics Framework for Evaluating Evolutionary Processes in Multi-objective Optimization](https://mlsanity.com/paper/2308.05640/) (2023)
- [FMMO: Detecting the Divergence Between Local Attribution and Global Drift](https://mlsanity.com/paper/2609.06173/) (2026)

## Abstract (arXiv)

Time-series data is widely studied in various scenarios, like weather forecast, stock market, customer behavior analysis. To comprehensively learn about the dynamic environments, it is necessary to comprehend features from multiple data sources. This paper proposes a novel visual analysis approach for detecting and analyzing concept drifts from multi-sourced time-series. We propose a visual detection scheme for discovering concept drifts from multiple sourced time-series based on prediction models. We design a drift level index to depict the dynamics, and a consistency judgment model to justify whether the concept drifts from various sources are consistent. Our integrated visual interface, ConceptExplorer, facilitates visual exploration, extraction, understanding, and comparison of concepts and concept drifts from multi-source time-series data. We conduct three case studies and expert interviews to verify the effectiveness of our approach.