DeepVATS: Deep Visual Analytics for Time Series

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...

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Vydané v:Knowledge-based systems Ročník 277; s. 110793
Hlavní autori: Rodriguez-Fernandez, Victor, Montalvo-Garcia, David, Piccialli, Francesco, Nalepa, Grzegorz J., Camacho, David
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 09.10.2023
Predmet:
Deep learning
Masked AutoEncoder
Time series
Visual analytics
Deep learning
Visual analytics
Masked AutoEncoder
Time series
ISSN:0950-7051, 1872-7409
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Popis
Shrnutí: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 an 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.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2023.110793