.png){kind=icon}
返回
DeepVATS: Deep Visual Analytics for Time Series
.png){kind=icon}
OA
AI
DOI：10.1016/j.knosys.2023.110793.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.(c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keyword:
Deep learning
Visual analytics
Time series
Masked AutoEncoder
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Synthesis of K-Carrageenan Flame-Retardant Microspheres and Its Application for Waterborne Epoxy Resin with Functionalized Graphene.png)
PolymersOAAI
PolymersIF0
OAAI分享收藏TimeCluster: dimension reduction applied to temporal data for visual analyticsVISUAL COMPUTEROAAI
VISUAL COMPUTERIF2.9
OAAI分享收藏Comparative visual analytics for assessing medical records with sequence embeddingVISUAL INFORMATICSOAAI
VISUAL INFORMATICSIF3.9
OAAI分享收藏
查看更多内容