# TEASER: Early and Accurate Time Series Classification

arXiv: https://arxiv.org/abs/1908.03405 · HTML: https://mlsanity.com/paper/1908.03405/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2019.

## Built on by (most cited first, 26 of 26)

- [dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification](https://mlsanity.com/paper/2207.12165/) (2022)
- [Stop&Hop: Early Classification of Irregular Time Series](https://mlsanity.com/paper/2208.09795/) (2022)
- [When is Early Classification of Time Series Meaningful?](https://mlsanity.com/paper/2102.11487/) (2021)
- [Snippet Policy Network for Multi-class Varied-length ECG Early Classification](https://mlsanity.com/paper/2107.13361/) (2021)
- [Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series](https://mlsanity.com/paper/2111.06032/) (2021)
- [Spectral Cross-Domain Neural Network with Soft-adaptive Threshold Spectral Enhancement](https://mlsanity.com/paper/2301.10171/) (2023)
- [Early Classification of Time Series: A Survey and Benchmark](https://mlsanity.com/paper/2406.18332/) (2024)
- [The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy Optimization](https://mlsanity.com/paper/2105.13636/) (2021)
- [On the Generalization and Approximation Capacities of Neural Controlled Differential Equations](https://mlsanity.com/paper/2305.16791/) (2023)
- [Incremental Sequence Classification with Temporal Consistency](https://mlsanity.com/paper/2505.16548/) (2025)
- [Early Classification of Time Series is Meaningful](https://mlsanity.com/paper/2104.13257/) (2021)
- [Motion Code: Robust Time Series Classification and Forecasting via Sparse Variational Multi-Stochastic Processes Learning](https://mlsanity.com/paper/2402.14081/) (2024)
- [ml_edm package: a Python toolkit for Machine Learning based Early Decision Making](https://mlsanity.com/paper/2408.12925/) (2024)
- [Alert: Learning Trigger Functions for Early Classification of Time Series using Deep-RL](https://mlsanity.com/paper/2502.06584/) (2025)
- [Early Detection of Patient Deterioration from Real-Time Wearable Monitoring System](https://mlsanity.com/paper/2505.01305/) (2025)
- [Early Time-Series Classification Algorithms: An Empirical Comparison](https://mlsanity.com/paper/2203.01628/) (2022)
- [Forecasting Early with Meta Learning](https://mlsanity.com/paper/2307.09796/) (2023)
- [Representation Learning of Tangled Key-Value Sequence Data for Early Classification](https://mlsanity.com/paper/2404.07454/) (2024)
- [Improving Time Series Classification with Representation Soft Label Smoothing](https://mlsanity.com/paper/2408.17010/) (2024)
- [Foreclassing: A new machine learning perspective on human decision making with temporal data](https://mlsanity.com/paper/2503.04956/) (2025)
- [TEA: Temporal Adaptive Satellite Image Semantic Segmentation](https://mlsanity.com/paper/2601.04956/) (2026)
- [Event-Based Early Warning of Vineyard Disease Risk from Environmental Time Series](https://mlsanity.com/paper/2605.04548/) (2026)
- [Generative Diffusion Prior Distillation for Long-Context Knowledge Transfer](https://mlsanity.com/paper/2605.11414/) (2026)
- [CC-AOS: Cost- and Horizon-Conditioned Amortized Backward Induction for Finite-Horizon Optimal Stopping](https://mlsanity.com/paper/2607.22774/) (2026)
- [Primal--Dual Alternating Neural Learning for Timely Classification with Performance Guarantees](https://mlsanity.com/paper/2608.23480/) (2026)
- [Supraglacial Lake Fate Is Knowable Long Before the Season Ends](https://mlsanity.com/paper/2608.30113/) (2026)

## Abstract (arXiv)

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.