# Early Classification of Time Series: A Survey and Benchmark

arXiv: https://arxiv.org/abs/2406.18332 · HTML: https://mlsanity.com/paper/2406.18332/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 77 references that are in the map.
- First posted on arXiv 26 Jun 2024.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control](https://mlsanity.com/paper/2110.01052/) (2021)
- [MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification](https://mlsanity.com/paper/2012.08791/) (2020)
- [Bake off redux: a review and experimental evaluation of recent time series classification algorithms](https://mlsanity.com/paper/2304.13029/) (2023)
- [aeon: a Python toolkit for learning from time series](https://mlsanity.com/paper/2406.14231/) (2024)
- [Approaches and Applications of Early Classification of Time Series: A Review](https://mlsanity.com/paper/2005.02595/) (2020)
- [An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Set](https://mlsanity.com/paper/2305.11921/) (2023)
- [TEASER: Early and Accurate Time Series Classification](https://mlsanity.com/paper/1908.03405/) (2019)
- [End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping](https://mlsanity.com/paper/1901.10681/) (2019)
- [WEASEL 2.0 -- A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series Classification](https://mlsanity.com/paper/2301.10194/) (2023)
- [Early Time Classification with Accumulated Accuracy Gap Control](https://mlsanity.com/paper/2402.00857/) (2024)
- [Stop&Hop: Early Classification of Irregular Time Series](https://mlsanity.com/paper/2208.09795/) (2022)
- [Classifiers With a Reject Option for Early Time-Series Classification](https://mlsanity.com/paper/1312.3989/) (2013)
- [When is Early Classification of Time Series Meaningful?](https://mlsanity.com/paper/2102.11487/) (2021)
- [Earliness-Aware Deep Convolutional Networks for Early Time Series Classification](https://mlsanity.com/paper/1611.04578/) (2016)
- [Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series](https://mlsanity.com/paper/2111.06032/) (2021)
- [Open challenges for Machine Learning based Early Decision-Making research](https://mlsanity.com/paper/2204.13111/) (2022)
- [Toward Asymptotic Optimality: Sequential Unsupervised Regression of Density Ratio for Early Classification](https://mlsanity.com/paper/2302.09810/) (2023)
- [A Policy for Early Sequence Classification](https://mlsanity.com/paper/2304.03463/) (2023)
- [Convolutional Neural Networks for Time-dependent Classification of Variable-length Time Series](https://mlsanity.com/paper/2207.03718/) (2022)

## Built on by (most cited first, 4 of 4)

- [Alert: Learning Trigger Functions for Early Classification of Time Series using Deep-RL](https://mlsanity.com/paper/2502.06584/) (2025)
- [Early Classification of Time Series in Non-Stationary Cost Regimes](https://mlsanity.com/paper/2602.00918/) (2026)
- [Primal--Dual Alternating Neural Learning for Timely Classification with Performance Guarantees](https://mlsanity.com/paper/2608.23480/) (2026)
- [Popularity Trajectories and Mixing Patterns of Urban Venues in Two European Cities](https://mlsanity.com/paper/2610.18917/) (2026)

## Abstract (arXiv)

In many situations, the measurements of a studied phenomenon are provided sequentially, and the prediction of its class needs to be made as early as possible so as not to incur too high a time penalty, but not too early and risk paying the cost of misclassification. This problem has been particularly studied in the case of time series, and is known as Early Classification of Time Series (ECTS). Although it has been the subject of a growing body of literature, there is still a lack of a systematic, shared evaluation protocol to compare the relative merits of the various existing methods. In this paper, we highlight the two components of an ECTS system: decision and prediction, and focus on the approaches that separate them. This document begins by situating these methods within a principle-based taxonomy. It defines dimensions for organizing their evaluation and then reports the results of a very extensive set of experiments along these dimensions involving nine state-of-the-art ECTS algorithms. In addition, these and other experiments can be carried out using an open-source library in which most of the existing ECTS algorithms have been implemented (see https://github.com/ML-EDM/ml_edm).