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Aggregated time series are generated effortlessly everywhere, e.g., "totalconfirmed covid-19 cases since 2019" and "total liquor sales over time."Understanding "how" and "why" these key performance indicators (KPI) evolveover time is critical to making data-informed decisions. Existing explanationengines focus on explaining one aggregated value or the difference between tworelations. However, this falls short of explaining KPIs' continuous changesover time. Motivated by this, we propose TSEXPLAIN, a system that explainsaggregated time series by surfacing the underlying evolving top contributors.Under the hood, we leverage prior works on two-relations diff as a buildingblock and formulate a K-Segmentation problem to segment the time series suchthat each segment after segmentation shares consistent explanations, i.e.,contributors. To quantify consistency in each segment, we propose a novelwithin-segment variance design that is explanation-aware; to derive the optimalK-Segmentation scheme, we develop an efficient dynamic programming algorithm.Experiments on synthetic and real-world datasets show that ourexplanation-aware segmentation can effectively identify evolving explanationsfor aggregated time series and outperform explanation-agnostic segmentation.Further, we proposed an optimal selection strategy of K and severaloptimizations to speed up TSEXPLAIN for interactive user experience, achievingup to 13X efficiency improvement.

TSEXPLAIN: Explaining Aggregated Time Series by Surfacing Evolving Contributors