TSEXPLAIN: Explaining Aggregated Time Series by Surfacing Evolving Contributors

TL;DR

TSEXPLAIN is a system that explains how aggregated time series data evolve over time by identifying and surfacing the key contributors responsible for changes, using an explanation-aware segmentation approach.

Contribution

It introduces a novel explanation-aware segmentation method for time series, leveraging a K-Segmentation formulation and dynamic programming for efficient, evolving contributor identification.

Findings

Effective in identifying evolving explanations in real-world data

Outperforms explanation-agnostic segmentation methods

Achieves up to 13X efficiency improvements

Abstract

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