Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Real-Time Anomaly Detection for Streaming Analytics   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.

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The categories of this paper are: cond-mat
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The categories of this paper are: q-fin
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The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
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The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-ex
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The categories of this paper are: physics
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The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an
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The categories of this paper are: ao-sci
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The categories of this paper are: eess
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The categories of this paper are: hep-th