# Online false discovery rate control for anomaly detection in time series

arXiv: https://arxiv.org/abs/2112.03196 · HTML: https://mlsanity.com/paper/2112.03196/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 6 Dec 2021.

## Builds on (most cited first, 16 of 16)

- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Time-Series Anomaly Detection Service at Microsoft](https://mlsanity.com/paper/1906.03821/) (2019)
- [Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress](https://mlsanity.com/paper/2009.13807/) (2020)
- [High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes](https://mlsanity.com/paper/1910.03002/) (2019)
- [Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art](https://mlsanity.com/paper/2004.00433/) (2020)
- [On the Nature and Types of Anomalies: A Review of Deviations in Data](https://mlsanity.com/paper/2007.15634/) (2020)
- [Online Rules for Control of False Discovery Rate and False Discovery Exceedance](https://mlsanity.com/paper/1603.09000/) (2016)
- [Choosing Between Methods of Combining p-values](https://mlsanity.com/paper/1707.06897/) (2017)
- [Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data](https://mlsanity.com/paper/1708.03665/) (2017)
- [Online control of the false discovery rate with decaying memory](https://mlsanity.com/paper/1710.00499/) (2017)
- [SAFFRON: an adaptive algorithm for online control of the false discovery rate](https://mlsanity.com/paper/1802.09098/) (2018)
- [ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls](https://mlsanity.com/paper/1905.11465/) (2019)
- [Asynchronous Online Testing of Multiple Hypotheses](https://mlsanity.com/paper/1812.05068/) (2018)
- [Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper](https://mlsanity.com/paper/2101.06054/) (2021)
- [On Online Control of False Discovery Rate](https://mlsanity.com/paper/1502.06197/) (2015)
- [The Power of Batching in Multiple Hypothesis Testing](https://mlsanity.com/paper/1910.04968/) (2019)

## Built on by (most cited first, 13 of 13)

- [Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency](https://mlsanity.com/paper/2206.08496/) (2022)
- [ADBench: Anomaly Detection Benchmark](https://mlsanity.com/paper/2206.09426/) (2022)
- [SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling](https://mlsanity.com/paper/2302.00861/) (2023)
- [Unsupervised Model Selection for Time-series Anomaly Detection](https://mlsanity.com/paper/2210.01078/) (2022)
- [Graph Anomaly Detection in Time Series: A Survey](https://mlsanity.com/paper/2302.00058/) (2023)
- [Deep Learning for Time Series Forecasting: A Survey](https://mlsanity.com/paper/2503.10198/) (2025)
- [CREMA: A Contrastive Regularized Masked Autoencoder for Robust ECG Diagnostics across Clinical Domains](https://mlsanity.com/paper/2407.07110/) (2024)
- [A Comprehensive Review of Trends, Applications and Challenges In Out-of-Distribution Detection](https://mlsanity.com/paper/2209.12935/) (2022)
- [Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition](https://mlsanity.com/paper/2505.01783/) (2025)
- [On the Adversarial Robustness of Learning-based Conformal Novelty Detection](https://mlsanity.com/paper/2510.00463/) (2025)
- [Online Reliable Anomaly Detection via Neuromorphic Sensing and Communications](https://mlsanity.com/paper/2510.14688/) (2025)
- [Dynamic Reward Scaling for Multivariate Time Series Anomaly Detection: A VAE-Enhanced Reinforcement Learning Approach](https://mlsanity.com/paper/2511.12351/) (2025)
- [Safe Urban Traffic Control via Uncertainty-Aware Conformal Prediction and World-Model Reinforcement Learning](https://mlsanity.com/paper/2602.04821/) (2026)

## Abstract (arXiv)

This article proposes novel rules for false discovery rate control (FDRC) geared towards online anomaly detection in time series. Online FDRC rules allow to control the properties of a sequence of statistical tests. In the context of anomaly detection, the null hypothesis is that an observation is normal and the alternative is that it is anomalous. FDRC rules allow users to target a lower bound on precision in unsupervised settings. The methods proposed in this article overcome short-comings of previous FDRC rules in the context of anomaly detection, in particular ensuring that power remains high even when the alternative is exceedingly rare (typical in anomaly detection) and the test statistics are serially dependent (typical in time series). We show the soundness of these rules in both theory and experiments.