# DeepVATS: Deep Visual Analytics for Time Series

arXiv: https://arxiv.org/abs/2302.03858 · HTML: https://mlsanity.com/paper/2302.03858/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 57 references that are in the map.
- First posted on arXiv 8 Feb 2023.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [A Transformer-based Framework for Multivariate Time Series Representation Learning](https://mlsanity.com/paper/2010.02803/) (2020)
- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [Not Just a Black Box: Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1605.01713/) (2016)
- [Embedding Projector: Interactive Visualization and Interpretation of Embeddings](https://mlsanity.com/paper/1611.05469/) (2016)
- [AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization](https://mlsanity.com/paper/2102.01330/) (2021)
- [A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization](https://mlsanity.com/paper/2012.00467/) (2020)
- [Sintel: A Machine Learning Framework to Extract Insights from Signals](https://mlsanity.com/paper/2204.09108/) (2022)
- [MTV: Visual Analytics for Detecting, Investigating, and Annotating Anomalies in Multivariate Time Series](https://mlsanity.com/paper/2112.05734/) (2021)
- [A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction](https://mlsanity.com/paper/2008.01645/) (2020)
- [Comparative Visual Analytics for Assessing Medical Records with Sequence Embedding](https://mlsanity.com/paper/2002.08356/) (2020)
- [PSEUDo: Interactive Pattern Search in Multivariate Time Series with Locality-Sensitive Hashing and Relevance Feedback](https://mlsanity.com/paper/2104.14962/) (2021)

## Built on by (most cited first, 4 of 4)

- [Data clustering: a fundamental method in data science and management](https://mlsanity.com/paper/2412.18760/) (2024)
- [Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics](https://mlsanity.com/paper/2504.20099/) (2025)
- [Exploring Scalability in Large-Scale Time Series in DeepVATS framework](https://mlsanity.com/paper/2408.04692/) (2024)
- [EXPRTS: Exploring and Probing the Robustness of Time Series Forecasting Models](https://mlsanity.com/paper/2403.03508/) (2024)

## Abstract (arXiv)

The field of Deep Visual Analytics (DVA) has recently arisen from the idea of developing Visual Interactive Systems supported by deep learning, in order to provide them with large-scale data processing capabilities and to unify their implementation across different data and domains. In this paper we present DeepVATS, an open-source tool that brings the field of DVA into time series data. DeepVATS trains, in a self-supervised way, a masked time series autoencoder that reconstructs patches of a time series, and projects the knowledge contained in the embeddings of that model in an interactive plot, from which time series patterns and anomalies emerge and can be easily spotted. The tool includes a back-end for data processing pipeline and model training, as well as a front-end with a interactive user interface. We report on results that validate the utility of DeepVATS, running experiments on both synthetic and real datasets. The code is publicly available on https://github.com/vrodriguezf/deepvats