Abstract: In accordance with the teachings described herein, systems and methods are provided for clustering time series based on forecast distributions. A method for clustering time series based on forecast distributions may include: receiving time series data relating to one or more aspects of a physical process; applying a forecasting model to the time series data to generate forecasted values and confidence intervals associated with the forecasted values, the confidence intervals being generated based on distribution information relating to the forecasted values; generating a distance matrix that identifies divergence in the forecasted values, the distance matrix being generated based the distribution information relating to the forecasted values; and performing a clustering operation on the plurality of forecasted values based on the distance matrix. The distance matrix may be generated using a symmetric Kullback-Leibler divergence algorithm.

Abstract: In accordance with the teachings described herein, systems and methods are provided for clustering time series based on forecast distributions. A method for clustering time series based on forecast distributions may include: receiving time series data relating to one or more aspects of a physical process; applying a forecasting model to the time series data to generate forecasted values and confidence intervals associated with the forecasted values, the confidence intervals being generated based on distribution information relating to the forecasted values; generating a distance matrix that identifies divergence in the forecasted values, the distance matrix being generated based the distribution information relating to the forecasted values; and performing a clustering operation on the plurality of forecasted values based on the distance matrix. The distance matrix may be generated using a symmetric Kullback-Leibler divergence algorithm.

Abstract: Computer-implemented systems and methods are provided for generating bins for a scorecard. An approximate set of bins is generated by applying an optimization model to binning data. The optimization model includes an objective function, constraints, and surrogate weight of evidence metric(s). The approximated set of bins are then used in scorecard operations.

Abstract: Computer-implemented systems and methods are provided for creating a cluster structure from a data set containing input variables. Global clusters are created within a first stage, by computing a similarity matrix from the data set. A global cluster structure and sub-cluster structure are created within a second stage, where the global cluster structure and the sub-cluster structure are created using a latent variable clustering technique and the cluster structure output is generated by combining the created global cluster structure and the created sub-cluster structure.

Abstract: Computer-implemented systems and methods are provided for creating a cluster structure from a data set containing input variables. Global clusters are created within a first stage, by computing a similarity matrix from the data set. A global cluster structure and sub-cluster structure are created within a second stage, where the global cluster structure and the sub-cluster structure are created using a latent variable clustering technique and the cluster structure output is generated by combining the created global cluster structure and the created sub-cluster structure.

Abstract: Computer-implemented systems and methods are provided for generating bins for a scorecard. An approximate set of bins is generated by applying an optimization model to binning data. The optimization model includes an objective function, constraints, and surrogate weight of evidence metric(s). The approximated set of bins are then used in scorecard operations.