DeepVATS: Deep Visual Analytics for Time Series
This provides a domain-specific tool for researchers and practitioners in time series analysis, offering an incremental advancement by applying deep visual analytics to a new data type.
The authors tackled the problem of analyzing time series data by developing DeepVATS, an open-source tool that uses a self-supervised masked autoencoder to reconstruct time series patches and projects embeddings into an interactive plot for pattern and anomaly detection, with validation on synthetic and real datasets.
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