US 2025/0028737 A1 · Anomaly Detection Through Clustering of Time-Series Data Subsequences and Determination of Adaptive Thresholding

Abstract

Computerized methodologies are disclosed that are directed to detecting anomalies within a time-series data set. An aspect of the anomaly detection process includes determining one or more seasonality patterns that correspond to a specific time-series data set by evaluating a set of candidate seasonality patterns (e.g., hourly, daily, weekly, day-start off-sets, etc.). The evaluation of a candidate seasonality pattern may include dividing the time-series data set into a collection of subsequences based on the particular candidate seasonality pattern. Further, the collection of subsequences may be divided into clusters and a silhouette score may be computed to measure the clustering quality of the candidate seasonality pattern. In some instances, the candidate seasonality pattern having the highest silhouette score is selected and utilized in anomaly detection process. In other instances, a plurality of seasonality patterns may be combined forming a time policy, where the time policy is utilized in anomaly detection process.

Claims20

Dependent claims

A computer-implemented method, comprising:

obtaining a time-series data set;
dividing the time-series data set into a set of subsequences based on a first candidate seasonality pattern;
clustering the set of subsequences in accordance with the first candidate seasonality pattern;
determining a silhouette score measuring a quality of the clustering, wherein the silhouette score indicates how well the first candidate seasonality pattern fits the time-series data set;
establishing an anomaly band for the time-series data set based on the first candidate seasonality pattern when the silhouette score satisfies a threshold comparison;
and
detecting one or more anomalies within the time-series data set that are outside of the anomaly band.

4 more elements

A non-transitory computer-readable medium having stored thereon instructions that, when executed by one or more processors, cause the one or more processor to perform operations including:

obtaining a time-series data set;
dividing the time-series data set into a set of subsequences based on a first candidate seasonality pattern;
clustering the set of subsequences in accordance with the first candidate seasonality pattern;
determining a silhouette score measuring a quality of the clustering, wherein the silhouette score indicates how well the first candidate seasonality pattern fits the time-series data set;
establishing an anomaly band for the time-series data set based on the first candidate seasonality pattern when the silhouette score satisfies a threshold comparison;
and
detecting one or more anomalies within the time-series data set that are outside of the anomaly band.

4 more elements

Dependencies are inferred from the claim text.

Anomaly Detection Through Clustering of Time-Series Data Subsequences and Determination of Adaptive Thresholding

Life of this patent

Anomaly Detection Through Clustering of Time-Series Data Subsequences and Determination of Adaptive Thresholding

Life of this patent

Abstract

Claims20