Framework for choosing the appropriate generalized linear model
AssigneeVerint Americas Inc.
InventorCynthia Freeman
Life of this patent
Estimating the term…
Abstract
Systems and methods are provided framework for automatically choosing the appropriate generalized linear model (GLM) given a time series of count data, and for anomaly detection on time series data. A dispersion parameter is determined and used to determine whether the count data is overdispersed data or underdispersed data. The overdispersed data or the underdispersed data is used to determine a GLM to apply on the dataset. Using the determined GLM on the data, anomalies can be determined.
Claims20
A method for determining a generalized linear model (GLM) to apply on a dataset, the method comprising:
- receiving a dataset comprising count data by a computing device;
- determining whether the count data is overdispersed data or underdispersed data by the computing device;
- and
- using the overdispersed data or the underdispersed data to determine a GLM to apply on the dataset by the computing device.
A system for determining a generalized linear model (GLM) to apply on a dataset, the system comprising:
- a dispersion determination module that determines whether the count data is overdispersed data or underdispersed data;
- and
- a GLM selector module that uses the overdispersed data or the underdispersed data to determine a GLM to apply on the dataset.
A method for determining an anomaly in a dataset, the method comprising:
- receiving a dataset comprising a time series of count data by a computing device;
- determining whether the count data is overdispersed data or underdispersed data by the computing device;
- using the overdispersed data or the underdispersed data to determine a generalized linear model (GLM) to apply on the dataset by the computing device;
- applying the GLM on the dataset to generate a result by the computing device;
- and
- determining that an anomaly exists in the dataset using the result by the computing device.
Dependencies are inferred from the claim text.