# ml_edm package: a Python toolkit for Machine Learning based Early Decision Making

arXiv: https://arxiv.org/abs/2408.12925 · HTML: https://mlsanity.com/paper/2408.12925/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 23 Aug 2024.

## Builds on (most cited first, 3 of 3)

- [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)
- [TEASER: Early and Accurate Time Series Classification](https://mlsanity.com/paper/1908.03405/) (2019)

## Built on by (most cited first, 1 of 1)

- [Alert: Learning Trigger Functions for Early Classification of Time Series using Deep-RL](https://mlsanity.com/paper/2502.06584/) (2025)

## Abstract (arXiv)

textttmledm is a Python 3 library, designed for early decision making of any learning tasks involving temporal/sequential data. The package is also modular, providing researchers an easy way to implement their own triggering strategy for classification, regression or any machine learning task. As of now, many Early Classification of Time Series (ECTS) state-of-the-art algorithms, are efficiently implemented in the library leveraging parallel computation. The syntax follows the one introduce in textttscikit-learn, making estimators and pipelines compatible with textttmledm. This software is distributed over the BSD-3-Clause license, source code can be found at urlhttps://github.com/ML-EDM/mledm.