Method for detecting anomalies in a time series data with trajectory and stochastic components
First Claim
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1. A method for detecting anomalies in time series data, comprising the steps of:
- partitioning the time series data into overlapping and sliding time windows;
determine features for each window;
clustering features of similar windows to obtain universal features, wherein the universal feature includes a stochastic component, wherein statistics of the stochastic component include a mean, a standard deviation, a mean of an absolute value of a first difference, a number of mean crossings, a percentage of positive values in the first difference, a percentage of zero values in the first difference, and an average length of a run of positive differences of the time series data in the window, and standard deviations of the statistics;
comparing the universal features extracted from testing time series data with the universal features acquired from training time series data to determine a score, wherein the universal features characterize trajectory components of the time series data and stochastic components of the time series data; and
detecting an anomaly if the anomaly score is above a threshold, wherein the steps are performed in a processor.
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Abstract
A method detects anomalies in time series data by comparing universal features extracted from testing time series data with the universal features acquired from training time series data to determine a score. The universal features characterize trajectory components of the time series data and stochastic components of the time series data. Then, an anomaly is detected if the anomaly score is above a threshold.
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6 Claims
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1. A method for detecting anomalies in time series data, comprising the steps of:
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partitioning the time series data into overlapping and sliding time windows; determine features for each window; clustering features of similar windows to obtain universal features, wherein the universal feature includes a stochastic component, wherein statistics of the stochastic component include a mean, a standard deviation, a mean of an absolute value of a first difference, a number of mean crossings, a percentage of positive values in the first difference, a percentage of zero values in the first difference, and an average length of a run of positive differences of the time series data in the window, and standard deviations of the statistics; comparing the universal features extracted from testing time series data with the universal features acquired from training time series data to determine a score, wherein the universal features characterize trajectory components of the time series data and stochastic components of the time series data; and detecting an anomaly if the anomaly score is above a threshold, wherein the steps are performed in a processor. - View Dependent Claims (2, 3, 4, 5, 6)
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Specification