Abstract: Systems and methods for forecasting ratios in hierarchies are provided. Hierarchies can be formed that have components, including a numerator time series with values from input data, a denominator time series with values from input data, and a ratio time series of the numerator time series over the denominator time series. The components can be modeled to generate forecasted hierarchies. The forecasted hierarchies can be reconciled so that the forecasted hierarchies are statistically consistent throughout nodes of the forecasted hierarchies.

Abstract: Systems and methods for forecasting ratios in hierarchies are provided. Hierarchies can be formed that have components, including a numerator time series with values from input data, a denominator time series with values from input data, and a ratio time series of the numerator time series over the denominator time series. The components can be modeled to generate forecasted hierarchies. The forecasted hierarchies can be reconciled so that the forecasted hierarchies are statistically consistent throughout nodes of the forecasted hierarchies.

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 evaluating a physical process with respect to one or more attributes of the physical process by combining forecasts for the one or more physical process attributes, where data for evaluating the physical process is generated over time. A forecast model selection graph is accessed, the forecast model selection graph comprising a hierarchy of nodes arranged in parent-child relationships. A plurality of model forecast nodes are resolved, where resolving a model forecast node includes generating a node forecast for the one or more physical process attributes. A combination node is processed, where a combination node transforms a plurality of node forecasts at child nodes of the combination node into a combined forecast. A selection node is processed, where a selection node chooses a node forecast from among child nodes of the selection node based on a selection criteria.

Abstract: Computer-implemented systems and methods to process time-series data. As an example, a system and method can use a first data store to store time series data, and a second data store to store definitions of events. A dummy variable is generated when data from an event's definition is applied to time series data.

Abstract: A computer-implemented method and system for large-scale automatic forecasting. The method and system determine which forecasting models in a pool of forecasting models may best predict input transactional data. Candidate models are selected from the pool of forecasting models by comparing characteristics of the models in the pool with characteristics of the input transaction data. To further reduce the number of models, hold-out sample analysis is performed for the candidate models. The candidate model(s) that best perform with respect to the hold-out sample analysis are used to generate forecasted output.