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    Technique for incremental and flexible detection and modeling of patterns in time series data
    62.
    发明授权
    Technique for incremental and flexible detection and modeling of patterns in time series data 有权

    公开(公告)号：US11023350B2

    公开(公告)日：2021-06-01

    申请号：US16256228

    申请日：2019-01-24

    Applicant: Oracle International Corporation

    Inventor： Sampanna Shahaji Salunke ,  Dustin Garvey ,  Sumathi Gopalakrishnan

    IPC: G06F11/34

    Abstract: The present disclosure describes a flexible technique to learn patterns in time series data that recur over time. The patterns may be used for simulation, predicting future behavior, or detecting anomalies in a system in which the data is collected. The technique incrementally detects daily, weekly, monthly, and yearly patterns. Each pattern is built over time instead of requiring all the data to be available at the beginning of the analysis. Instead of modeling each pattern explicitly, each pattern is described in the context of a day and formed based on time series data collected over an entire day. An example use of the technique is detecting load patterns in a computer system. A metric of system load such as CPU utilization may be collected periodically over a day. The techniques presented herein capture multiple daily models, each representing a different load pattern.

    Unsupervised method for classifying seasonal patterns
    66.
    发明授权
    Unsupervised method for classifying seasonal patterns 有权

    公开(公告)号：US10885461B2

    公开(公告)日：2021-01-05

    申请号：US15057062

    申请日：2016-02-29

    Applicant: Oracle International Corporation

    Inventor： Dustin Garvey ,  Uri Shaft ,  Lik Wong

    IPC: G06N20/00

    Abstract: Techniques are described for classifying seasonal patterns in a time series. In an embodiment, a set of time series data is decomposed to generate a noise signal and a dense signal, where the noise signal includes a plurality of sparse features from the set of time series data and the dense signal includes a plurality of dense features from the set of time series data. A set of one or more sparse features from the noise signal is selected for retention. After selecting the sparse features, a modified set of time series data is generated by combining the set of one or more sparse features with a set of one or more dense features from the plurality of dense features. At least one seasonal pattern is identified from the modified set of time series data. A summary for the seasonal pattern may then be generated and stored.

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