DeepVATS: Deep Visual Analytics for Time Series

Abstract

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

Submitted to Elsevier's Knowledge Based Systems journal. Code available at https://github.com/vrodriguezf/deepvats

References in corpus (6)

• Time Series Forecasting With Deep Learning: A Survey
• InceptionTime: Finding AlexNet for Time Series Classification
• Embedding Projector: Interactive Visualization and Interpretation of Embeddings
• A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization
• Sintel: A Machine Learning Framework to Extract Insights from Signals
• Plotly-Resampler: Effective Visual Analytics for Large Time Series

Cited by in corpus (3)

• Data clustering: a fundamental method in data science and management
• Time Series Information Visualization -- A Review of Approaches and Tools
• Exploring Scalability in Large-Scale Time Series in DeepVATS framework