Title: m-TSNE: A Framework for Visualizing High-Dimensional Multivariate Time Series,
Abstract: Multivariate time series (MTS) have become increasingly common in healthcare
domains where human vital signs and laboratory results are collected for
predictive diagnosis. Recently, there have been increasing efforts to visualize
healthcare MTS data based on star charts or parallel coordinates. However, such
techniques might not be ideal for visualizing a large MTS dataset, since it is
difficult to obtain insights or interpretations due to the inherent high
dimensionality of MTS. In this paper, we propose 'm-TSNE': a simple and novel
framework to visualize high-dimensional MTS data by projecting them into a
low-dimensional (2-D or 3-D) space while capturing the underlying data
properties. Our framework is easy to use and provides interpretable insights
for healthcare professionals to understand MTS data. We evaluate our
visualization framework on two real-world datasets and demonstrate that the
results of our m-TSNE show patterns that are easy to understand while the other
methods' visualization may have limitations in interpretability.