TSEXPLAIN: Surfacing Evolving Explanations for Time Series

Understanding the underlying explanations for what has happened is more and more crucial in today's business decision-making processes. Existing explanation engines focus on explaining the difference between two given sets. However, for time-series, the explanations usually evolve as time advances. Thus, only considering two end timestamps would miss all explanations in between. To mitigate this, we demonstrate TSEXPLAIN, a system to help users understand the underlying evolving explanations for any aggregated time-series. Internally, TSEXPLAIN models the explanation problem as a segmentation problem over the time dimension and uses existing works on two-sets diff as building blocks. In our demonstration, conference attendees will be able to easily and interactively explore the evolving explanations and visualize how these explanations contribute to the overall changes in various datasets: COVID-19, S&P500, Iowa Liquor Sales. Questions-like "which states make COVID-19 total confirmed case number go up dramatically during the past year?", "which stocks drive the dramatic crashes of S&P500 in Mar and the quick rebound later?", and "how does Liquor sales' trend look like from January 2020 till now and why" can all get well answered by TSEXPLAIN.