ANOMALY DETECTION FOR NON-STATIONARY DATA
First Claim
1. A method comprising:
- extracting a training time series corresponding to a process from an initial time series corresponding to the process, the training time series including a subset of the initial time series, the subset of the initial time series having a length offset by an index prior to a last data point of the initial time series;
modifying outlier data points in the training time series based on predetermined acceptability criteria;
training a plurality of prediction methods using the training time series;
receiving an actual data point corresponding to the initial time series, the actual data point having an index after the last data point of the training time series;
using the plurality of prediction methods to determine a set of predicted data points corresponding to the actual data point of the initial time series; and
determining whether the actual data point is anomalous based on a calculation of whether each of the set of predicted data points is statistically different from the actual data point.
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Abstract
A method of detecting anomalies in a time series is disclosed. A training time series corresponding to a process is extracted from an initial time series corresponding to the process, the training time series including a subset of the initial time series. Outlier data points in the training time series are modified based on predetermined acceptability criteria. A plurality of prediction methods are trained using the training time series. An actual data point corresponding to the initial time series is received. The plurality of prediction methods are used to determine a set of predicted data points corresponding to the actual data point. It is determined whether the actual data point is anomalous based on a calculation of whether each of the set of predicted data points is statistically different from the actual data point.
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Citations
20 Claims
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1. A method comprising:
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extracting a training time series corresponding to a process from an initial time series corresponding to the process, the training time series including a subset of the initial time series, the subset of the initial time series having a length offset by an index prior to a last data point of the initial time series; modifying outlier data points in the training time series based on predetermined acceptability criteria; training a plurality of prediction methods using the training time series; receiving an actual data point corresponding to the initial time series, the actual data point having an index after the last data point of the training time series; using the plurality of prediction methods to determine a set of predicted data points corresponding to the actual data point of the initial time series; and determining whether the actual data point is anomalous based on a calculation of whether each of the set of predicted data points is statistically different from the actual data point. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A system comprising:
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one or more modules implemented by one or more processors, the one or more modules configured to; extract a training time series corresponding to a process from an initial time series corresponding to the process, the training time series including a subset of the initial time series, the subset of the initial time series having a length offset by an index prior to a last data point of the initial time series; modify outlier data points in the training time series based on predetermined acceptability criteria; train a plurality of prediction methods using the training time series; receive an actual data point corresponding to the initial time series, the actual data point having an index after the last data point of the training time series; use the plurality of prediction methods to determine a set of predicted data points corresponding to the actual data point of the initial time series; and determine whether the actual data point is anomalous based on a calculation of whether each of the set of predicted data points is statistically different from the actual data point. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A non-transitory machine readable medium comprising a set of instructions that, when executed by a processor, causes the processor to perform operations, the operations comprising:
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extracting a training time series corresponding to a process from an initial time series corresponding to the process, the training time series including a subset of the initial time series, the subset of the initial time series having a length offset by an index prior to a last data point of the initial time series; modifying outlier data points in the training time series based on predetermined acceptability criteria; training a plurality of prediction methods using the training time series; receiving an actual data point corresponding to the initial time series, the actual data point having an index after the last data point of the training time series; using the plurality of prediction methods to determine a set of predicted data points corresponding to the actual data point of the initial time series; and determining whether the actual data point is anomalous based on a calculation of whether each of the set of predicted data points is statistically different from the actual data point. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification