Visualizing Multimodal Time Series at Scale

Jiahang Huang, Andrew Ni, Jasmine Zhang, Hongyi Zhu, Hui Zhang

Abstract: Today digital recording technology empowers us to understand real-world behaviors with high quality and high definition multimodal time series data. Making the presentation of these time series fit for analysis purpose, at the right scale and resolution, has become a leading data visualization challenge. In this paper, we present TimeXplore, a novel visual analysis tool to aid the exploration of time series at scale. TimeXplore allows one to query and navigate large volumes of time series and their aggregates in near real time, with a simple yet powerful interface. The visualization synchronized across modalities can provide still further capability for us to develop and verify our hypothesis in multimodal data analysis.