mlsanityData to 14 Oct 2026

DeepVATS: Deep Visual Analytics for Time Series

arXiv 2302.03858 ↗First posted 8 Feb 2023cs.LGcited by 4

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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

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

17 of 57 references.

References · 17

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  3. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
  4. 2020Self-supervised Learning: Generative or Contrastivecited by 694
  5. 2018Deep learning for time series classification: a reviewcited by 576
  6. 2020Time Series Forecasting With Deep Learning: A Surveycited by 459
  7. 2020A Transformer-based Framework for Multivariate Time Series Representation Learningcited by 453
  8. 2019InceptionTime: Finding AlexNet for Time Series Classificationcited by 387
  9. 2016Not Just a Black Box: Learning Important Features Through Propagating Activation Differencescited by 297
  10. 2016Embedding Projector: Interactive Visualization and Interpretation of Embeddingscited by 44
  11. 2021AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualizationcited by 32
  12. 2020A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualizationcited by 28
5 more
  1. 2022Sintel: A Machine Learning Framework to Extract Insights from Signalscited by 16
  2. 2021MTV: Visual Analytics for Detecting, Investigating, and Annotating Anomalies in Multivariate Time Seriescited by 11
  3. 2020A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reductioncited by 9
  4. 2020Comparative Visual Analytics for Assessing Medical Records with Sequence Embeddingcited by 4
  5. 2021PSEUDo: Interactive Pattern Search in Multivariate Time Series with Locality-Sensitive Hashing and Relevance Feedbackcited by 2

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Cited by year

Citing papers per year: 2024: 3; 2025: 1.

4 papers.

  1. 2024Data clustering: a fundamental method in data science and managementcited by 2
  2. 2025Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analyticscited by 2
  3. 2024Exploring Scalability in Large-Scale Time Series in DeepVATS frameworkcited by 1
  4. 2024EXPRTS: Exploring and Probing the Robustness of Time Series Forecasting Modelscited by 0