$d_{symb}$ playground: an interactive tool to explore large multivariate time series datasets

Links: paper / PDF / code / Streamlit app / 4 min YouTube video.

Abstract:

Exploring and comparing non-stationary multivariate time series is an important problem in many domains and real-world applications. In recent work, we introduced $d_{symb}$, a symbolic representation that transforms multivariate time series into interpretable symbolic sequences that comes along with a compatible and efficient distance measure to compare the obtained symbolic sequences. We have shown how $d_{symb}$ can handle the non-stationarity of multivariate physiological signals, how interpretable the symbolization is, and how suitable the distance measure is compared to Dynamic Time Warping (DTW) variants. We have also empirically shown that the computation time when using $d_{symb}$ on a clustering time is significantly smaller than with DTW variants (typically 100 times faster). In this demonstration, we present the $d_{symb}$ playground, an interactive web-based tool to interpret and compare a large multivariate time series dataset quickly. We showcase the relevance of this tool in several scenarios based on real-world datasets.