# Early Classification of Time Series is Meaningful

arXiv: https://arxiv.org/abs/2104.13257 · HTML: https://mlsanity.com/paper/2104.13257/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 27 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [TEASER: Early and Accurate Time Series Classification](https://mlsanity.com/paper/1908.03405/) (2019)
- [Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoring](https://mlsanity.com/paper/1905.09568/) (2019)
- [Alarm-Based Prescriptive Process Monitoring](https://mlsanity.com/paper/1803.08706/) (2018)
- [When is Early Classification of Time Series Meaningful?](https://mlsanity.com/paper/2102.11487/) (2021)
- [Early Classification for Agricultural Monitoring from Satellite Time Series](https://mlsanity.com/paper/1908.10283/) (2019)
- [Earliness-Aware Deep Convolutional Networks for Early Time Series Classification](https://mlsanity.com/paper/1611.04578/) (2016)

## Built on by (most cited first, 2 of 2)

- [Open challenges for Machine Learning based Early Decision-Making research](https://mlsanity.com/paper/2204.13111/) (2022)
- [Multivariate Time Series Early Classification Across Channel and Time Dimensions](https://mlsanity.com/paper/2306.14606/) (2023)

## Abstract (arXiv)

Many approaches have been proposed for early classification of time series in light of its significance in a wide range of applications including healthcare, transportation and finance. However, recently a preprint saved on Arxiv claim that all research done for almost 20 years now on the Early Classification of Time Series is useless, or, at the very least, ill-oriented because severely lacking a strong ground. In this paper, we answer in detail the main issues and misunderstandings raised by the authors of the preprint, and propose directions to further expand the fields of application of early classification of time series.