TEASER: early and accurate time series classification

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 widely between time series. We present TEASER, a novel algorithm that models eTSC as a 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.

MultiETSC: automated machine learning for early time series classification

“…This confidence is then used in the triggering mechanism. SR-CF (Mori et al 2018), TEASER (Schäfer and Leser 2020) and ECEC (Lv et al 2019) are all extensions of this idea using slightly different triggering mechanisms and prefix-classifiers. EARLIEST (Hartvigsen et al 2019) is based on a recurrent neural network (RNN) with LSTM cells.…”

“…To address the second question, we compared our multi-objective approach with a method that optimises a single objective (we refer to this baseline as SO-all). For this objective, we chose the harmonic mean of earliness and accuracy as suggested by Schäfer and Leser (2020):…”

“…Each hyperparameter setting results in a classifier whose performance is represented by a single point in the earliness-accuracy space. Figure 1a shows the earliness and accuracy of a set of manually configured algorithms, for each of which five distinct values of the trade-off hyperparameter have been considered, evenly spread over the full range of the hyperparameter (we chose to consider five settings since that number corresponds closely to what is reported in literature (e.g., Mori et al 2018;Schäfer and Leser 2020;Mori et al 2019) and it would be a realistic number of configurations to evaluate manually).…”

“…A way around this challenge would be to combine both objectives into a single objective. Within the context of EarlyTSC, using the harmonic mean of earliness and accuracy as an objective function has been proposed (Schäfer and Leser 2020). However, this approach fails to capture the complex trade-off between earliness and accuracy.…”

Early Detection of Sexual Predators in Chats

“…An interesting perspective on our Tier 2 is that it actually solves an early time series classification (eTSC) problem, for which there exist several mature approaches, e.g. TEASER (Schäfer and Leser, 2020) or ECTS (Xing et al, 2012). However, there exists a key difference that prevents us from using such methods directly: An eSPD System never classifies a chat as nongrooming as long as there are still messages left (or expected), while an eTSC system at some stage might decide that it is safe to stop controlling the chat (Loyola et al, 2018).…”