TEASER: Early and Accurate Time Series Classification

TL;DR

TEASER is a novel early time series classification algorithm that adaptively determines when enough data has been collected for accurate predictions, outperforming existing methods in timeliness and accuracy across benchmarks and real-world applications.

Contribution

TEASER introduces a two-tier classification approach that adaptively decides the optimal decision time for each time series, addressing arbitrary start times in real-world scenarios.

Findings

TEASER predicts earlier than competitors while maintaining or improving accuracy.

TEASER achieves two to three times earlier predictions on benchmark datasets.

TEASER demonstrates superior performance in energy monitoring and gait detection applications.

Abstract

Early time series classification (eTSC) is the problem of classifying a time series after as few measurements as possible with the highest possible accuracy. The most critical issue of any eTSC method is to decide when enough data of a time series has been seen to take a decision: Waiting for more data points usually makes the classification problem easier but delays the time in which a classification is made; in contrast, earlier classification has to cope with less input data, often leading to inferior accuracy. The state-of-the-art eTSC methods compute a fixed optimal decision time assuming that every times series has the same defined start time (like turning on a machine). However, in many real-life applications measurements start at arbitrary times (like measuring heartbeats of a patient), implying that the best time for taking a decision varies heavily between time series. We present TEASER, a novel algorithm that models eTSC as a two two-tier classification problem: In the first tier, a classifier periodically assesses the incoming time series to compute class probabilities. However, these class probabilities are only used as output label if a second-tier classifier decides that the predicted label is reliable enough, which can happen after a different number of measurements. In an evaluation using 45 benchmark datasets, TEASER is two to three times earlier at predictions than its competitors while reaching the same or an even higher classification accuracy. We further show TEASER's superior performance using real-life use cases, namely energy monitoring, and gait detection.