Deniz Senturk-Doganaksoy - Danbury CT, US Christina LaComb - Schenectady NY, US Marat Doganaksoy - Danbury CT, US
Assignee:
GENERAL ELECTRIC COMPANY - SCHENECTADY NY
International Classification:
G06Q 40/00
US Classification:
705035000
Abstract:
A method for predicting the financial health of a business entity is provided. The method comprises generating one or more anomaly scores and one or more multi-dimensional time-varying patterns for one or more financial metrics related to a business entity and analyzing the one or more anomaly scores and the one or more multi-dimensional time-varying patterns for the one or more financial metrics, using a dynamic predictive modeling system. The method then comprises predicting one or more business behavioral patterns related to the business entity based on the step of analyzing and aggregating the one or more predicted business behavioral patterns in a selected manner to predict the financial health of the business entity.
Deniz Senturk-Doganaksoy - Danbury CT, US Andrew J. Travaly - Ballston Spa NY, US Richard J. Rucigay - Saratoga Springs NY, US Christina Ann LaComb - Schenectady NY, US Peter T. Skowronek - Marietta GA, US
International Classification:
G06F 9/44
US Classification:
705 7
Abstract:
A method for determining whether an operational metric representing the performance of a target machine has an anomalous value is provided. The method includes collecting operational data from at least one machine, and calculating at least one exceptional anomaly score from the obtained operational data.
Deniz Senturk-Doganaksoy - Danbury CT, US Christina A. LaComb - Schenectady NY, US Richard J. Rucigay - Saratoga Springs NY, US Peter T. Skowronek - Marietta GA, US Andrew J. Travaly - Ballston Spa NY, US
International Classification:
G06F 9/44
US Classification:
705 7
Abstract:
A method for aggregating anomalous values is provided. The method comprises obtaining operational data from at least one machine and calculating at least one exceptional anomaly score from the operational data. The exceptional anomaly scores can then be aggregated to identify acute or chronic anomalous values.