Abstract: A computer-implemented system and method for evaluating customer activity. Data about the customer activity is received and is used to generate actual data values associated with preselected business metrics. One or more business metric score cards may be generated to assess how the business metrics are performing as well as what business metrics can be changed to better meet business goals.

Abstract: A computer-implemented system and method for evaluating customer activity. Data about the customer activity is received and is used to generate actual data values associated with preselected business metrics. One or more business metric score cards may be generated to assess how the business metrics are performing as well as what business metrics can be changed to better meet business goals.

Abstract: A computer-implemented system and method for evaluating customer activity. Data about the customer activity is received and is used to generate actual data values associated with preselected business metrics. One or more business metric score cards may be generated to assess how the business metrics are performing as well as what business metrics can be changed to better meet business goals.

Abstract: A computer-implemented system and method for evaluating customer activity. Data about the customer activity is received and is used to generate actual data values associated with preselected business metrics. One or more business metric score cards may be generated to assess how the business metrics are performing as well as what business metrics can be changed to better meet business goals.

Abstract: A method for selecting node variables in a binary decision tree structure is provided. The binary decision tree is formed by mapping node variables to known outcome variables. The method calculates a statistical measure of the significance of each input variable in an input data set and then selects an appropriate node variable on which to base the structure of the binary decision tree using an averaged statistical measure of the input variable and any co-linear input variables of the data set.

Abstract: A computer-implemented system and method for evaluating customer activity. Data about the customer activity is received and is used to generate actual data values associated with preselected business metrics. One or more business metric score cards may be generated to assess how the business metrics are performing as well as what business metrics can be changed to better meet business goals.

Abstract: A computer-implemented method and system for building a neural network is disclosed. The neural network predicts at least one target based upon predictor variables defined in a state space. First, an input data set is retrieved that includes the predictor variables and at least one target associated with the predictor variables for each observation. In the state space, a number of points is inserted in the state space based upon the values of the predictor variables. The number of points is less than the number of observations. A statistical measure is determined that describes a relationship between the observations and the inserted points. Weights and activation functions of the neural network are determined using the statistical measure.

Abstract: Computer-implemented systems and methods for analyzing time series data. Statistical techniques are performed upon candidate autoregressive components and regressor components using the time series data. Autoregressive and regressor components are included in a predictive model based upon the autoregressive and regressor components' significance levels as determined by the statistical techniques.

Abstract: A computer-implemented method and system for building a neural network is disclosed. The neural network predicts at least one target based upon predictor variables defined in a state space. First, an input data set is retrieved that includes the predictor variables and at least one target associated with the predictor variables for each observation. In the state space, a number of points is inserted in the state space based upon the values of the predictor variables. The number of points is less than the number of observations. A statistical measure is determined that describes a relationship between the observations and the inserted points. Weights and activation functions of the neural network are determined using the statistical measure.

Abstract: A method for selecting node variables in a binary decision tree structure is provided. The binary decision tree is formed by mapping node variables to known outcome variables. The method calculates a statistical measure of the significance of each input variable in an input data set and then selects an appropriate node variable on which to base the structure of the binary decision tree using an averaged statistical measure of the input variable and any co-linear input variables of the data set.

Abstract: A computer-implemented method and system for building a neural network is disclosed. The neural network predicts at least one target based upon predictor variables defined in a state space. First, an input data set is retrieved that includes the predictor variables and at least one target associated with the predictor variables for each observation. In the state space, a number of points is inserted in the state space based upon the values of the predictor variables. The number of points is less than the number of observations. A statistical measure is determined that describes a relationship between the observations and the inserted points. Weights and activation functions of the neural network are determined using the statistical measure.

Abstract: A computer-implemented system and method for evaluating customer activity. Data about the customer activity is received and is used to generate actual data values associated with preselected business metrics. One or more business metric score cards may be generated to assess how the business metrics are performing as well as what business metrics can be changed to better meet business goals.

Abstract: A method for selecting node variables in a binary decision tree structure is provided. The binary decision tree is formed by mapping node variables to known outcome variables. The method calculates a statistical measure of the significance of each input variable in an input data set and then selects an appropriate node variable on which to base the structure of the binary decision tree using an averaged statistical measure of the input variable and any co-linear input variables of the data set.

Abstract: A method for selecting node variables in a binary decision tree structure is provided. The binary decision tree is formed by mapping node variables to known outcome variables. The method calculates a statistical measure of the significance of each input variable in an input data set and then selects an appropriate node variable on which to base the structure of the binary decision tree using an averaged statistical measure of the input variable and any co-linear input variables of the data set.

Abstract: A method for selecting node variables in a binary decision tree structure is provided. The binary decision tree is formed by mapping node variables to known outcome variables. The method calculates a statistical measure of the significance of each input variable in an input data set and then selects an appropriate node variable on which to base the structure of the binary decision tree using an averaged statistical measure of the input variable and any co-linear input variables of the data set.

Abstract: A computer-implemented method and system for building a neural network is disclosed. The neural network predicts at least one target based upon predictor variables defined in a state space. First, an input data set is retrieved that includes the predictor variables and at least one target associated with the predictor variables for each observation. In the state space, a number of points is inserted in the state space based upon the values of the predictor variables. The number of points is less than the number of observations. A statistical measure is determined that describes a relationship between the observations and the inserted points. Weights and activation functions of the neural network are determined using the statistical measure.