Branch and Border: Partition-Based Change Detection in Multivariate Time Series

Given multivariate time series data, how do we detect changes in the behavior of the time series: for example, the onset of illnesses or complications in patients? Can we do this without making strong assumptions about the data? We propose BnB (Branch and Border), an online, nonparametric change detection method that detects multiple changes in multivariate data. Unlike existing methods, BnB approaches change detection by separating points before and after the change using an ensemble of random partitions. BnB is (a) scalable: it scales linearly in the number of time ticks and dimensions, and is online, thus using bounded memory and bounded time per iteration; (b) effective: providing theoretical guarantees on the false positive rate, and achieving 70% or more increased F-measure over baselines in experiments averaged over 11 datasets; (c) general: it is nonparametric, and works on mixed data, including numerical, categorical, and ordinal data.