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    Unsupervised method for classifying seasonal patterns
    31.
    发明授权
    Unsupervised method for classifying seasonal patterns 有权

    公开(公告)号：US11836162B2

    公开(公告)日：2023-12-05

    申请号：US16862496

    申请日：2020-04-29

    Applicant: Oracle International Corporation

    Inventor： Dustin Garvey ,  Uri Shaft ,  Lik Wong

    IPC: G06F16/28 ,  G06N20/00

    CPC classification number: G06F16/285 ,  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.

    SYSTEMS AND METHODS FOR MULTIVARIATE ANOMALY DETECTION IN SOFTWARE MONITORING
    33.
    发明申请
    SYSTEMS AND METHODS FOR MULTIVARIATE ANOMALY DETECTION IN SOFTWARE MONITORING 有权

    公开(公告)号：US20230075486A1

    公开(公告)日：2023-03-09

    申请号：US18055773

    申请日：2022-11-15

    Applicant: Oracle International Corporation

    Inventor： Sampanna Shahaji Salunke ,  Dario Bahena Tapia ,  Dustin Garvey ,  Sumathi Gopalakrishnan ,  Neil Goodman

    IPC: H04L9/40 ,  G06N20/10 ,  G06K9/62

    Abstract: Techniques are disclosed for summarizing, diagnosing, and correcting the cause of anomalous behavior in computing systems. In some embodiments, a system identifies a plurality of time series that track different metrics over time for a set of one or more computing resources. The system detects a first set of anomalies in a first time series that tracks a first metric and assigns a different respective range of time to each anomaly. The system determines whether the respective range of time assigned to an anomaly overlaps with timestamps or ranges of time associated with anomalies from one or more other time series. The system generates at least one cluster that groups metrics based on how many anomalies have respective ranges of time and/or timestamps that overlap. The system may preform, based on the cluster, one or more automated actions for diagnosing or correcting a cause of anomalous behavior.

    UNSUPERVISED METHOD FOR BASELINING AND ANOMALY DETECTION IN TIME-SERIES DATA FOR ENTERPRISE SYSTEMS
    35.
    发明申请
    UNSUPERVISED METHOD FOR BASELINING AND ANOMALY DETECTION IN TIME-SERIES DATA FOR ENTERPRISE SYSTEMS 有权

    公开(公告)号：US20210320939A1

    公开(公告)日：2021-10-14

    申请号：US17356186

    申请日：2021-06-23

    Applicant: Oracle International Corporation

    Inventor： Sampanna Shahaji Salunke ,  Dustin Garvey ,  Uri Shaft ,  Maria Kaval

    IPC: H04L29/06 ,  H04L12/26 ,  G06K9/62

    Abstract: Systems and methods for performing unsupervised baselining and anomaly detection using time-series data are described. In one or more embodiments, a baselining and anomaly detection system receives a set of time-series data. Based on the set of time-series, the system generates a first interval that represents a first distribution of sample values associated with the first seasonal pattern and a second interval that represents a second distribution of sample values associated with the second seasonal pattern. The system then monitors a time-series signals using the first interval during a first time period and the second interval during a second time period. In response to detecting an anomaly in the first seasonal pattern or the second seasonal pattern, the system performs a responsive action, such as generating an alert.

    Systems and methods for forecasting time series with variable seasonality
    36.
    发明授权
    Systems and methods for forecasting time series with variable seasonality 有权

    公开(公告)号：US11138090B2

    公开(公告)日：2021-10-05

    申请号：US16168390

    申请日：2018-10-23

    Applicant: Oracle International Corporation

    Inventor： Dustin Garvey ,  Sampanna Shahaji Salunke ,  Uri Shaft ,  Sumathi Gopalakrishnan

    IPC: G06F11/34 ,  G06F17/18 ,  G06F16/28 ,  G06K9/62 ,  G06N20/00 ,  G06N7/00

    Abstract: Techniques for training and evaluating seasonal forecasting models are disclosed. In some embodiments, a network service generates, in memory, a set of data structures that separate sample values by season type and season space. The set of data structures may include a first set of clusters corresponding to different season types in the first season space and a second set of clusters corresponding to different season types in the second season space. The network service merges two or more clusters the first set and/or second set of clusters. Clusters from the first set are not merged with clusters from the second set. After merging the clusters, the network service determines a trend pattern for each of the remaining clusters in the first and second set of clusters. The network service then generates a forecast for a metric of a computing resource based on the trend patterns for each remaining cluster.

    Optimization for scalable analytics using time series models
    39.
    发明授权
    Optimization for scalable analytics using time series models 有权

    公开(公告)号：US10949436B2

    公开(公告)日：2021-03-16

    申请号：US15902830

    申请日：2018-02-22

    Applicant: Oracle International Corporation

    Inventor： Sampanna Shahaji Salunke ,  Dustin Garvey ,  Michael Avrahamov

    IPC: G06F16/2458 ,  G06F12/0802 ,  G06F16/2455 ,  G06F16/182

    Abstract: Techniques are described for optimizing scalability of analytics that use time-series models. In one or more embodiments, a stored time-series model includes a plurality of data points representing seasonal behavior in a training set of time-series data for at least one season. A target time for evaluating the time-series model is then determined, and the target time or one or more times relative to the target time are mapped to a subset of the plurality of data points. Based on the mapping, a trimmed version of the time-series model is generated by loading the subset of the plurality of data points into a cache, the subset of data points representing seasonal behavior in the training set of time-series data for a portion of the at least one season. A target set of time-series data may be evaluated suing the trimmed version of the time-series in the cache.

    Multiscale method for predictive alerting
    40.
    发明授权
    Multiscale method for predictive alerting 有权

    公开(公告)号：US10915830B2

    公开(公告)日：2021-02-09

    申请号：US15643179

    申请日：2017-07-06

    Applicant: Oracle International Corporation

    Inventor： Dustin Garvey ,  Sampanna Shahaji Salunke ,  Uri Shaft ,  Amit Ganesh ,  Sumathi Gopalakrishnan

    IPC: G06N7/00 ,  G08B21/18 ,  G06N5/04 ,  G06N20/00

    Abstract: Techniques are described for generating predictive alerts. In one or more embodiments, a seasonal model is generated, the seasonal model representing one or more seasonal patterns within a first set of time-series data, the first set of time-series data comprising data points from a first range of time. A trend-based model is also generated to represent trending patterns within a second set of time-series data comprising data points from a second range of time that is different than the first range of time. A set of forecasted values is generated based on the seasonal model and the trend-based model. Responsive to determining that a set of alerting thresholds has been satisfied based on the set of forecasted values, an alert is generated.

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