# Approaches and Applications of Early Classification of Time Series: A Review

arXiv: https://arxiv.org/abs/2005.02595 · HTML: https://mlsanity.com/paper/2005.02595/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2020.

## Built on by (most cited first, 30 of 40)

- [Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data](https://mlsanity.com/paper/2309.05305/) (2023)
- [Unsupervised Representation Learning for Time Series: A Review](https://mlsanity.com/paper/2308.01578/) (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)
- [Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection](https://mlsanity.com/paper/2412.15668/) (2024)
- [Open challenges for Machine Learning based Early Decision-Making research](https://mlsanity.com/paper/2204.13111/) (2022)
- [When Federated Learning Meets Quantum Computing: Survey and Research Opportunities](https://mlsanity.com/paper/2504.08814/) (2025)
- [Continuous Diagnosis and Prognosis by Controlling the Update Process of Deep Neural Networks](https://mlsanity.com/paper/2210.02719/) (2022)
- [Automatically Reconciling the Trade-off between Prediction Accuracy and Earliness in Prescriptive Business Process Monitoring](https://mlsanity.com/paper/2307.05939/) (2023)
- [Early Classification of Time Series: A Survey and Benchmark](https://mlsanity.com/paper/2406.18332/) (2024)
- [Revisiting Backdoor Attacks on Time Series Classification in the Frequency Domain](https://mlsanity.com/paper/2503.09712/) (2025)
- [Confidence-Guided Learning Process for Continuous Classification of Time Series](https://mlsanity.com/paper/2208.06883/) (2022)
- [Graph Generation Powered with LLMs for Boosting Multivariate Time-Series Representation Learning](https://mlsanity.com/paper/2403.03645/) (2024)
- [Unsupervised detection and open-set classification of fast-ramped flexibility activation events](https://mlsanity.com/paper/2111.02174/) (2021)
- [Toward Asymptotic Optimality: Sequential Unsupervised Regression of Density Ratio for Early Classification](https://mlsanity.com/paper/2302.09810/) (2023)
- [DE3S: Dual-Enhanced Soft-Sparse-Shape Learning for Medical Early Time-Series Classification](https://mlsanity.com/paper/2510.12214/) (2025)
- [InstructTime++: Time Series Classification with Multimodal Language Modeling via Implicit Feature Enhancement](https://mlsanity.com/paper/2601.14968/) (2026)
- [EMSDialog: Synthetic Multi-person Emergency Medical Service Dialogue Generation from Electronic Patient Care Reports via Multi-LLM Agents](https://mlsanity.com/paper/2604.07549/) (2026)
- [Curricular and Cyclical Loss for Time Series Learning Strategy](https://mlsanity.com/paper/2312.15853/) (2023)
- [ml_edm package: a Python toolkit for Machine Learning based Early Decision Making](https://mlsanity.com/paper/2408.12925/) (2024)
- [CAND: Cross-Domain Ambiguity Inference for Early Detecting Nuanced Illness Deterioration](https://mlsanity.com/paper/2501.16365/) (2025)
- [Alert: Learning Trigger Functions for Early Classification of Time Series using Deep-RL](https://mlsanity.com/paper/2502.06584/) (2025)
- [Anomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach](https://mlsanity.com/paper/2502.15793/) (2025)
- [Early Detection of Patient Deterioration from Real-Time Wearable Monitoring System](https://mlsanity.com/paper/2505.01305/) (2025)
- [SigTime: Learning and Visually Explaining Time Series Signatures](https://mlsanity.com/paper/2512.12076/) (2025)
- [Early Time-Series Classification Algorithms: An Empirical Comparison](https://mlsanity.com/paper/2203.01628/) (2022)
- [When to Classify Events in Open Times Series?](https://mlsanity.com/paper/2204.00392/) (2022)
- [Forecasting Early with Meta Learning](https://mlsanity.com/paper/2307.09796/) (2023)
- [ChemTime: Rapid and Early Classification for Multivariate Time Series Classification of Chemical Sensors](https://mlsanity.com/paper/2312.09871/) (2023)
- [Representation Learning of Tangled Key-Value Sequence Data for Early Classification](https://mlsanity.com/paper/2404.07454/) (2024)

## Abstract (arXiv)

Early classification of time series has been extensively studied for minimizing class prediction delay in time-sensitive applications such as healthcare and finance. A primary task of an early classification approach is to classify an incomplete time series as soon as possible with some desired level of accuracy. Recent years have witnessed several approaches for early classification of time series. As most of the approaches have solved the early classification problem with different aspects, it becomes very important to make a thorough review of the existing solutions to know the current status of the area. These solutions have demonstrated reasonable performance in a wide range of applications including human activity recognition, gene expression based health diagnostic, industrial monitoring, and so on. In this paper, we present a systematic review of current literature on early classification approaches for both univariate and multivariate time series. We divide various existing approaches into four exclusive categories based on their proposed solution strategies. The four categories include prefix based, shapelet based, model based, and miscellaneous approaches. The authors also discuss the applications of early classification in many areas including industrial monitoring, intelligent transportation, and medical. Finally, we provide a quick summary of the current literature with future research directions.