TIME SERIES-BASED ENTITY BEHAVIOR CLASSIFICATION

• US 20140074614A1
• Filed: 03/14/2013
• Published: 03/13/2014
• Est. Priority Date: 09/12/2012
• Status: Abandoned Application

• Current Assignee: Amplero Inc.
• Original Assignee: Globys Inc
• Inventors: Mehanian, Courosh, Downs, Oliver, Cazzanti, Luca, Penzotti, Julie, Feng, Jackson
• Application Number:
  US13/830,957
• Publication Number:
  US 20140074614A1
• Time in Patent Office: Days
• US Class Current: 705/14.64
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0267 Wireless devices

Abstract

Techniques are disclosed that leverage time series techniques to express entity-activity data in a longitudinal temporal form, which may then be employed to dynamically classify the entity'"'"'s behavior. In some embodiments, groupings or segmentations of different entities that exhibit similar profiles of longitudinal temporal form are identified using various techniques, including frequency-domain analysis, and/or unsupervised model-based clustering. The clustering of entities enables directing of offerings to, for example, a telecommunication'"'"'s customer based on characteristics of the cluster.

Claims

1. A network device, comprising:

a transceiver to send and receive data over a network; and

a processor that is operative to perform actions, comprising;

receiving telecommunications customer data for a plurality of customers;

extracting from the data a time series for each of the plurality of customers;

computing for each of the plurality of customers, spectral content for each time series data within a time window;

performing a grouping from the spectral content to generate a plurality of groups; and

classifying each customer time series within one of the plurality of groups, the groups usable to dynamically market to at least one customer identified by a cluster.

2. The network device of claim 1, wherein performing a grouping from spectral content comprises an unsupervised clustering to generate a plurality of clusters, the groupings being identified with the clusters generated by the unsupervised clustering.

3. The network device of claim 1, wherein performing a grouping from spectral content comprises a supervised classification into a plurality of user-prescribed classes, the groupings being identified with the classes.

4. The network device of claim 1, wherein extracting a time series for each of the plurality of customers, further comprises performing an interpolation of each time series to a uniform time grid, using at least one of a smoothing, an abrupt, or a hybrid interpolation algorithm.

5. The network device of claim 1, wherein computing spectral content for each time series is based on determining coefficients of a Fourier series from each time series, and employing a complex moduli of the coefficients as spectral coefficients expressing spectral content of a each time series.

6. The network device of claim 3, wherein computing the spectral content further comprises performing an aggregation of the spectral coefficients for a time series.

7. The network device of claim 1, wherein performing grouping comprises selecting a number of groups to be used, based on employing a test set of data to compute a delta log-likelihood function for the test set of data, and selecting the number of groups when the delta log-likelihood function falls below a specified threshold value.

8. The network device of claim 1, wherein generating a plurality of groups comprises applying an expectation-maximization algorithm to a mixture model to generate the plurality of groups, and wherein classifying each customer further comprises computing likelihoods under the mixture model of the spectral coefficient representation for each customer, and associating the customer to the group with the largest likelihood among the plurality of groups.

9. The network device of claim 8, wherein the mixture model is a Gaussian mixture model.

10. A system, comprising:

one or more non-transitory storage devices usable to store customer data; and

one or more processors operative to perform actions, comprising;

receiving telecommunications customer data for a plurality of customers;

extracting from the data a time series for each of the plurality of customers;

computing for each of the plurality of customers, spectral content for each time series data within a time window;

performing grouping from the spectral content to generate a plurality of groups; and

classifying each customer time series within one of the plurality of groups, the groups usable to dynamically market to at least one customer identified by a group.

11. The system of claim 10, wherein extracting a time series for each of the plurality of customers, further comprises performing an interpolation of each time series to a uniform time grid, using at least one of a smoothing, an abrupt, or a hybrid interpolation algorithm.

12. The system of claim 10, wherein computing spectral content for each time series is based on determining coefficients of a Fourier transform from each time series, and employing a complex moduli of the coefficients as spectral coefficients expressing spectral content of a each time series.

13. The system of claim 12, wherein computing the spectral content further comprises performing an aggregation of the spectral coefficients for a time series.

14. The system of claim 10, wherein performing clustering grouping comprises selecting a number of groups to be used, based on employing a test set of data to compute a delta log-likelihood function for the test set of data, and selecting the number of groups when the delta log-likelihood function falls below a specified threshold value.

15. The system of claim 10, wherein generating a plurality of groups comprises applying an expectation-maximization algorithm to a mixture model to generate the plurality of groups.

16. The system of claim 15, wherein the mixture model is a Gaussian mixture model.

17. The system of claim 14, wherein classifying each customer further comprises computing likelihoods under the mixture model of the spectral coefficient representation for each customer, and associating the customer to the group with the largest likelihood among the plurality of groups.

18. An apparatus comprising a non-transitory computer readable medium, having computer-executable instructions stored thereon, that in response to execution by a computing device, cause the computing device to perform operations, comprising:

receiving telecommunications customer data for a plurality of customers;

extracting from the data a time series for each of the plurality of customers;

computing for each of the plurality of customers, spectral content for each time series data within a time window;

performing an unsupervised clustering from the spectral content to generate a plurality of clusters; and

classifying each customer time series within one of the plurality of clusters, the clusters usable to dynamically market to at least one customer identified by a cluster.

19. The apparatus of claim 18, wherein extracting a time series for each of the plurality of customers, further comprises performing an interpolation of each time series to a uniform time grid, using at least one of a smoothing, an abrupt, or a hybrid interpolation algorithm.

20. The apparatus of claim 18, wherein computing spectral content for each time series is based on determining coefficients of a Fourier series from each time series, and employing a complex moduli of the coefficients as spectral coefficients expressing spectral content of a each time series.

21. The apparatus of claim 18, wherein computing the spectral content further comprises performing an aggregation of the spectral coefficients for a time series.

22. The apparatus of claim 18, wherein performing an unsupervised clustering comprises selecting a number of clusters to be used, based on employing a test set of data to compute a delta log-likelihood function for the test set of data, and selecting the number of clusters when the delta log-likelihood function falls below a specified threshold value.

23. The apparatus of claim 18, wherein generating a plurality of clusters comprises applying an expectation-maximization algorithm to a Gaussian mixture model to generate the plurality of clusters.

24. The apparatus of claim 23, wherein classifying each customer further comprises computing likelihoods under the mixture model of the spectral coefficient representation for each customer, and associating the customer to the group with the largest likelihood among the plurality of clusters.