Identifying optimal multi-scale patterns in time-series streams

Abstract

A method, system, and computer readable medium for identifying local patterns in at least one time series data stream are disclosed. The method comprises generating multiple ordered levels of hierarchal approximation functions. The multiple ordered levels are generated directly from at least one given time series data stream including at least one set of time series data. The hierarchical approximation functions for each level of the multiple levels is based upon creating a set of approximating functions. The hierarchical approximation functions are also based upon selecting a current window with a current window length from a set of varying window lengths. The current window is selected for a current level of the multiple levels.

Description

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT [0001]This invention was made with Government support under Contract No.: H98230-05-3-0001 awarded by Intelligence Agencies. The Government has certain rights in this invention.FIELD OF THE INVENTION [0002]The present invention generally relates to the field of stream processing systems, and more particularly relates to identifying optimal local patterns in a data stream time series.BACKGROUND OF THE INVENTION [0003]Data streams have recently received much attention in several communities (e.g., theory, databases, networks, data mining) because of several important applications (e.g., network traffic analysis, moving object tracking, financial data analysis, sensor monitoring, environmental monitoring, scientific data processing). Many recent efforts concentrate on summarization and pattern discovery in time series data streams. Some of these recent efforts are further described in (Y. Chen, G. Dong, J. Han, B. W. Wah, ...

AI Technical Summary

Benefits of technology

[0012]One advantage of the present invention is that an optimal orthonormal transform is determined from data itself, as opposed to using a predetermined basis or approximating function (such as piecewise constant, short-window Fourier or wavelets). Another advantage of the present invention is that it provides a hierarchical, recursive summarization or approximation of the stream that examines the time series at multiple time scales (i.e., window sizes) and efficiently discovers the key patterns in each, as well as the key windows. Besides providing insight about the behavior of the time series by concisely describing the main trends in a time series, the discovered patterns can also be used to facilitate further data processing.

is that an optimal orthonormal transform is determined from data itself, as opposed to using a predetermined basis or approximating function (such as piecewise constant, short-window Fourier or wavelets). Another advantage of the present invention is that it provides a hierarchical, recursive summarization or approximation of the stream that examines the time series at multiple time scales (i.e., window sizes) and efficiently discovers the key patterns in each, as well as the key windows. Besides providing insight about the behavior of the time series by concisely describing the main trends in a time series, the discovered patterns can also be used to facilitate further data processing.

Problems solved by technology

Wavelets are by nature multi-scale, but they still use a fixed set of bases, which is also often hard to choose.

However, this approach does not produce a window size.

Also, this approach it is not applicable to streams, it is severely restricted in the type of approximation (each window is approximated by a discrete value, based on the vector quantization output) and hence the method cannot be composed so the next level reuses the approximations of the previous level.

This approach examines only a single, a priori chosen window size, and the computation required is too costly to be feasible in a streaming environment.

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