# Accurate Evaluation of Quickest Changepoint Detectors via Non-parametric Survival Analysis

arXiv: https://arxiv.org/abs/2605.18798 · HTML: https://mlsanity.com/paper/2605.18798/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 113 references that are in the map.
- First posted on arXiv 11 May 2026.

## Builds on (most cited first, 15 of 15)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark](https://mlsanity.com/paper/1510.03336/) (2015)
- [Precision and Recall for Time Series](https://mlsanity.com/paper/1803.03639/) (2018)
- [An Evaluation of Change Point Detection Algorithms](https://mlsanity.com/paper/2003.06222/) (2020)
- [MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection](https://mlsanity.com/paper/1909.09347/) (2019)
- [Multiple Change-point Detection: a Selective Overview](https://mlsanity.com/paper/1512.04093/) (2015)
- [Bandit Quickest Changepoint Detection](https://mlsanity.com/paper/2107.10492/) (2021)
- [A Log-Linear Non-Parametric Online Changepoint Detection Algorithm based on Functional Pruning](https://mlsanity.com/paper/2302.02718/) (2023)
- [Novelty Detection in Network Traffic: Using Survival Analysis for Feature Identification](https://mlsanity.com/paper/2301.06229/) (2023)
- [Spoken language change detection inspired by speaker change detection](https://mlsanity.com/paper/2302.05265/) (2023)
- [High Probability Latency Sequential Change Detection over an Unknown Finite Horizon](https://mlsanity.com/paper/2408.05817/) (2024)
- [SoftED: Metrics for Soft Evaluation of Time Series Event Detection](https://mlsanity.com/paper/2304.00439/) (2023)
- [Exact Distribution of the Generalized Shiryaev-Roberts Stopping Time Under the Minimax Brownian Motion Setup](https://mlsanity.com/paper/1601.04262/) (2016)
- [Change Point Detection in Dynamic Graphs with Decoder-only Latent Space Model](https://mlsanity.com/paper/2404.04719/) (2024)
- [Change-Point Detection in Industrial Data Streams based on Online Dynamic Mode Decomposition with Control](https://mlsanity.com/paper/2407.05976/) (2024)

## Abstract (arXiv)

We propose non-parametric estimators for the average run length (ARL) and average detection delay (ADD) in quickest changepoint detection (QCD) under finite and irregular sequence lengths. Although ARL and ADD are widely used as optimality criteria in theoretical and simulation studies, their application to real-world datasets is hindered by limited and irregular sequence lengths. To address this issue, we propose non-parametric estimators for the ARL and ADD, termed KM-ARL and KM-ADD, by drawing an analogy between QCD and survival analysis to model detection probabilities under sequence truncation. We derive estimation bias bounds and prove that they are asymptotically unbiased unless extrapolation is required. Experiments on simulated and real-world datasets demonstrate their practical utility, enhancing robustness against limited and irregular sequence lengths, improving interpretability, and facilitating empirical, intuitive model selection. Our Python code is provided at https://github.com/TaikiMiyagawa/Kaplan-Meier-Average-Run-Length, offering ready-to-use implementations for practitioners.