Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for determining a future conversion curve between a first metric and a second metric are provided. A system and method can include receiving a first metric and a second metric. A plurality of conversion curves are retrieved, where each conversion curve has a corresponding weight. One or more paths between the first metric and the second metric are calculated using two or more conversion curves, where each path begins with the first metric and ends with the second metric, and where each path is associated with a path weight based upon the sum of the weights used to calculate the path. A shortest path between the first metric and the second metric is determined, where the shortest path is the path having the least weight, and a future conversion curve is generated by collapsing the two or more conversion curves of the shortest path.

Abstract: 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.