REVIEW 2 cited by
Real-Time Anomaly Detection for Streaming Analytics
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original 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.
Forward citations
Cited by 2 Pith papers
-
Learn Faster and Remember More: Balancing Exploration and Exploitation for Continual Test-time Adaptation
A mean-teacher framework with multi-level consistency regularization and complementary anchor replay improves continual test-time adaptation by balancing exploration and exploitation.
-
A Hybrid Framework for Real-Time Data Drift and Anomaly Identification Using Hierarchical Temporal Memory and Statistical Tests
A hybrid HTM plus SPRT pipeline is proposed for real-time drift detection, with a neural network combiner for multivariate anomaly detection, but quantitative superiority over baselines is not demonstrated.
Discussion (0). Continue with ORCID to comment.