Seeq Corporation

Aswin Nataraj Venkat

Brian Parsonnet

Joseph R DiSantis

Eric K Parsonnet

A system for processing multivariate, time-series data to classify portions of the data corresponding to an identified event or condition. Time periods of interest may be identified as search criteria for a variety of purposes. As an example, an event or condition may occur, whereby, precursors to the event or condition may be present in the time-series data, and thereby be a time period of interest as a predictor of future occurrences. As another example, the state of the system during normal operation may be identified. Based on the provided data, the processing system may output a prediction or a likelihood of a re-occurrence of the condition. One or more actions may be taken in response to the output of the system to prevent the occurrence of the condition or otherwise respond to the possibility of the occurrence of the condition.

Multivariate time-series search and classification learning methods and systems

The effective dimensionality of multivariate data can be reduced while minimizing loss of fidelity to primary calculations of interest. The reduced dimensionality may simplify analysis and make it more computationally efficient as a result of the simplification. Boundaries along a subset of dimensions may be identified and used to effectively reduce the dimensionality of the data according to those boundaries.

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Belmont, CA, United States of America

Aswin Nataraj Venkat