BeatLex: Summarizing and Forecasting Time Series with Patterns

Given time-series data such as electrocardiogram (ECG) readings, or motion capture data, how can we succintly summarize the data in a way that robustly identifies patterns that appear repeatedly? How can we then use such a summary to identify anomalies such as abnormal heartbeats, and also forecast future values of the time series? Our main idea is a vocabulary-based approach, which automatically learns a set of common patterns, or ‘beat patterns,’ which are used as building blocks to describe the time series in an intuitive and interpretable way. Our summarization algorithm, BeatLex (Beat Lexicons for Summarization) is: (1) fast and online, requiring linear time in the data size and bounded memory; (2) effective, outperforming competing algorithms in labelling accuracy by \(5.3 \) times, and forecasting accuracy by \(1.8 \) times; (3) principled and parameter-free, as it is based on the Minimum Description Length principle of summarizing the data by compressing it using as few bits as possible, and automatically tunes all its parameters; (4) general: it applies to any domain of time series data, and can make use of multidimensional (i.e. coevolving) time series.