Sas Institute Inc.

Keith Eugene Crowe

Michael James Leonard

David Bruce Elsheimer

Stacey Michelle Christian

Edward Tilden Blair

Jennifer Leigh Sloan Beeman

Wilma Stainback Jackson

Rajesh S Selukar

Systems and methods are provided for analyzing through one-pass of unstructured time stamped data of a physical process. A distribution of time-stamped unstructured data is analyzed to identify a plurality of potential hierarchical structures for the unstructured data. A hierarchical analysis of the potential hierarchical structures is performed to determine an optimal frequency and a data sufficiency metric for the potential hierarchical structures. One of the potential hierarchical structures is selected as a selected hierarchical structure based on the data sufficiency metrics. The unstructured data is structured according to the selected hierarchical structure and the optimal frequency associated with the selected hierarchical structure, where said structuring of the unstructured data is performed via a single pass though the unstructured data. The identified statistical analysis of the physical process is performed using the structured data.

Computer-implemented systems and methods for efficient structuring of time series data

Systems and methods are provided for segmenting time-series data stored in data segments containing one or more data records. A combined segment error measure is determined based on a proposed combination of two candidate segments. An error cost to merge the two candidate segments is determined based on a difference between the combined segment error measure and a segment error measure of one of the segments. The two candidate segments are combined when the error cost to merge meets a merge threshold to generate a combined segment.

Computer-implemented systems and methods for flexible definition of time intervals

Computer-implemented systems and methods for processing time series data that is indicative of a data generation activity occurring over a period of time. A model specification hierarchical data structure is used for storing characteristics that define a time series model. A fitted model hierarchical data structure stores characteristics that define a fitted time series model. The characteristics of the fitted time series model are defined through an application of the time series model to the time series data.

Computer-implemented system and method for storing data analysis models

Computer-implemented systems and methods for performing forecast scoring based upon time series data that is indicative of a data generation activity occurring over a period of time. One or more data stores store historical data and future data. The historical data includes historical exogenous inputs. The future data includes future exogenous inputs. A dynamic statistical model receives the historical data and future data that are stored in the one or more data stores. The dynamic statistical model is to provide forecast scoring based upon the historical exogenous inputs and the future exogenous inputs. An optimizer having an objective function iteratively varies the future data, so that forecasts associated with the varying future data values are generated by the dynamic statistical model for evaluation by the objective function.

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Cary, NC, United States of America

Keith Eugene Crowe