# Supervised Anomaly Detection in Uncertain Pseudoperiodic Data Streams

arXiv: https://arxiv.org/abs/1607.05909 · HTML: https://mlsanity.com/paper/1607.05909/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2016.

## Built on by (most cited first, 4 of 4)

- [SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection](https://mlsanity.com/paper/2305.00550/) (2023)
- [Semi-supervised Variational Temporal Convolutional Network for IoT Communication Multi-anomaly Detection](https://mlsanity.com/paper/2104.01813/) (2021)
- [Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2506.00188/) (2025)
- [GeoChemAD: Benchmarking Unsupervised Geochemical Anomaly Detection for Mineral Exploration](https://mlsanity.com/paper/2603.13068/) (2026)

## Abstract (arXiv)

Uncertain data streams have been widely generated in many Web applications. The uncertainty in data streams makes anomaly detection from sensor data streams far more challenging. In this paper, we present a novel framework that supports anomaly detection in uncertain data streams. The proposed framework adopts an efficient uncertainty pre-processing procedure to identify and eliminate uncertainties in data streams. Based on the corrected data streams, we develop effective period pattern recognition and feature extraction techniques to improve the computational efficiency. We use classification methods for anomaly detection in the corrected data stream. We also empirically show that the proposed approach shows a high accuracy of anomaly detection on a number of real datasets.