Real-Time Anomaly Detection for Streaming Analytics
arXiv:1607.02480
Abstract
Much of the worlds data is streaming, time-series data, where anomalies give significant information in critical situations. Yet detecting anomalies in streaming data is a difficult task, requiring detectors to process data in real-time, and learn while simultaneously making predictions. We present a novel anomaly detection technique based on an on-line sequence memory algorithm called Hierarchical Temporal Memory (HTM). We show results from a live application that detects anomalies in financial metrics in real-time. We also test the algorithm on NAB, a published benchmark for real-time anomaly detection, where our algorithm achieves best-in-class results.
References in corpus (6)
- Bayesian Online Changepoint Detection
- Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark
- Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex
- Continuous online sequence learning with an unsupervised neural network model
- Finding Anomalous Periodic Time Series: An Application to Catalogs of Periodic Variable Stars
- How do neurons operate on sparse distributed representations? A mathematical theory of sparsity, neurons and active dendrites