Abstract: A method of converting data to tree data is provided. A first node memory structure that includes a first value indicator, a first counter value, and a first observation indicator is initialized for a first variable. The first value indicator is initialized with a first value of the first variable selected from first observation data, and the first observation indicator is initialized with a first indicator that indicates the first observation data. The first value of the first variable is compared to a second value of the first variable. The first counter value included in the first node memory structure is incremented when the first value of the first variable matches the second value of the first variable. Corresponding values of second observation data are compared to the identified values from first observation data when the first value of the first variable matches the second value of the first variable. A next observation is read from the data when the identified values match the corresponding values.

Abstract: A method of creating a contingency table is provided. Whether or not a variable level list exists for a second variable in tree data is determined. When the variable level list exists for the second variable in the tree data, a first node memory structure is determined for the second variable from the variable level list, a first value of a first variable is determined using a first observation indicator and the tree data, and a first counter value is added to the contingency table in association with the first value of the first variable and a first value of the second variable. The first node memory structure includes the first value indicator, the first counter value, and the first observation indicator. The first value indicator indicates a first value of the second variable.

Abstract: Computer-implemented systems and methods are provided for predicting outputs. Global output fractions associated with an object are approximated. Outputs for a group are predicted based upon a cyclical aspect component and a movement prediction. An output prediction is calculated based upon the predicted outputs for a related object group and the approximated global output fraction for a particular object.

Abstract: A method of converting data to tree data is provided. A first node memory structure that includes a first value indicator, a first counter value, and a first observation indicator is initialized for a first variable. The first value indicator is initialized with a first value of the first variable selected from first observation data, and the first observation indicator is initialized with a first indicator that indicates the first observation data. The first value of the first variable is compared to a second value of the first variable. The first counter value included in the first node memory structure is incremented when the first value of the first variable matches the second value of the first variable. Corresponding values of second observation data are compared to the identified values from first observation data when the first value of the first variable matches the second value of the first variable. A next observation is read from the data when the identified values match the corresponding values.

Abstract: A method of creating a contingency table is provided. Whether or not a variable level list exists for a second variable in tree data is determined. When the variable level list exists for the second variable in the tree data, a first node memory structure is determined for the second variable from the variable level list, a first value of a first variable is determined using a first observation indicator and the tree data, and a first counter value is added to the contingency table in association with the first value of the first variable and a first value of the second variable. The first node memory structure includes the first value indicator, the first counter value, and the first observation indicator. The first value indicator indicates a first value of the second variable.

Abstract: A method of selecting a one-class support vector machine (SVM) model for incremental response modeling is provided. Exposure group data generated from first responses by an exposure group receiving a request to respond is received. Control group data generated from second responses by a control group not receiving the request to respond is received. A response is either positive or negative. A one-class SVM model is defined using the positive responses in the control group data and an upper bound parameter value. The defined one-class SVM model is executed with the identified positive responses from the exposure group data. An error value is determined based on execution of the defined one-class SVM model. A final one-class SVM model is selected by validating the defined one-class SVM model using the determined error value.

Abstract: Computer-implemented systems and methods are provided to perform accuracy analysis with respect to forecasting models, wherein the forecasting models provide predictions based upon a pool of production data. As an example, a forecast accuracy monitoring system is provided to monitor the accuracy of the forecasting models over time based upon the pool of production data. A forecast model construction system builds and rebuilds the forecasting models based upon the pool of production data.

Abstract: Computer-implemented systems and methods are provided for forecasting product sales. Market shares associated with a product are estimated. Sales for a share group are forecast based upon a seasonality component and a trend prediction. A product sales forecast is calculated based upon the forecasted sales for a share group and the estimated product market share.

Abstract: Computer-implemented systems and methods are provided to perform accuracy analysis with respect to forecasting models, wherein the forecasting models provide predictions based upon a pool of production data. As an example, a forecast accuracy monitoring system is provided to monitor the accuracy of the forecasting models over time based upon the pool of production data. A forecast model construction system builds and rebuilds the forecasting models based upon the pool of production data.