# Alert: Learning Trigger Functions for Early Classification of Time Series using Deep-RL

arXiv: https://arxiv.org/abs/2502.06584 · HTML: https://mlsanity.com/paper/2502.06584/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 50 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 26 of 26)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Offline Reinforcement Learning with Implicit Q-Learning](https://mlsanity.com/paper/2110.06169/) (2021)
- [A Minimalist Approach to Offline Reinforcement Learning](https://mlsanity.com/paper/2106.06860/) (2021)
- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [Efficient Online Reinforcement Learning with Offline Data](https://mlsanity.com/paper/2302.02948/) (2023)
- [MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification](https://mlsanity.com/paper/2012.08791/) (2020)
- [Benchmarking Multivariate Time Series Classification Algorithms](https://mlsanity.com/paper/2007.13156/) (2020)
- [Revisiting the Minimalist Approach to Offline Reinforcement Learning](https://mlsanity.com/paper/2305.09836/) (2023)
- [When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?](https://mlsanity.com/paper/2204.05618/) (2022)
- [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)
- [Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data](https://mlsanity.com/paper/2210.08642/) (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)
- [Open challenges for Machine Learning based Early Decision-Making research](https://mlsanity.com/paper/2204.13111/) (2022)
- [Using PCA to Efficiently Represent State Spaces](https://mlsanity.com/paper/1505.00322/) (2015)
- [Simple Ingredients for Offline Reinforcement Learning](https://mlsanity.com/paper/2403.13097/) (2024)
- [Early Classification of Time Series: A Survey and Benchmark](https://mlsanity.com/paper/2406.18332/) (2024)
- [Toward Asymptotic Optimality: Sequential Unsupervised Regression of Density Ratio for Early Classification](https://mlsanity.com/paper/2302.09810/) (2023)
- [ml_edm package: a Python toolkit for Machine Learning based Early Decision Making](https://mlsanity.com/paper/2408.12925/) (2024)

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

- [Early Classification of Time Series in Non-Stationary Cost Regimes](https://mlsanity.com/paper/2602.00918/) (2026)

## Abstract (arXiv)

Early Classification of Time Series (ECTS) is vital in fields like industrial monitoring and medical triage, where quick and accurate predictions are essential. One of the core challenges lies in the trigger function, which decides when to make a prediction, independently of the classifier itself. Most existing methods rely on handcrafted rules, but can data-driven approaches outperform them? This paper introduces Alert, a Deep-RL framework that learns trigger functions from any state representation. Systematic comparisons on 30 datasets show that the design of the state space significantly influences performance. Building on this, we propose Alert+, a simple yet effective variant that consistently outperforms traditional methods in balancing accuracy and delay within an imbalanced misclassification and exponential delay cost setting. Alert and Alert+ are released to support reproducible research and practical applications.