Abstract: An example embodiment may involve a machine learning model representing relationships between a dependent variable and a plurality of n independent variables. The dependent variable may be a function of the n independent variables, where the n independent variables are measurable characteristics of computing devices, and where the dependent variable is a predicted behavior of the computing devices. The embodiment may also involve obtaining a target value of the dependent variable, and separating the n independent variables into n?1 independent variables with fixed values and a particular independent variable with an unfixed value. The embodiment may also involve performing a partial inversion of the function to produce a value of the particular independent variable such that, when the function is applied to the value of the particular independent variable and the n?1 independent variables with fixed values, the dependent variable is within a pre-defined range of the target value.

Abstract: The present invention envisages a system and method for automating the generation of business decision analytic models. The system uses a plurality of predictor variables stored in a plurality of data sets, to automatically create a business decision analytic model. The system includes a processor configured to process the data sets and determine the total number of records present in each of the data sets and the number of columns containing only numerical values. The processor selects a column containing only numerical values, from a dataset under consideration, and counts the number of unique numerical values in the selected column, and the total number of records present in the selected column. The two counts are compared and the selected column is transformed using a non-linear transformation to obtain a column of transformed values. The transformed values and corresponding time stamps are utilized for the purpose of model generation.

Abstract: A computer implemented system for automating the generation of an analytic model includes a processor configured to process a plurality of data sets. Each data set includes values for a plurality of variables. A time-stamping module is configured to derive values for a plurality of elapsed-time variables for each data set, and the plurality of variables and plurality of elapsed-time variables are included in a plurality of model variables. A model generator is configured to create a plurality of comparison analytic models each based on a different subset of model variables. Each comparison analytic model is configured to operate on new data sets associated with current leads, and to output a likelihood of successfully closing an associated transaction. A model testing module is configured to select an operational analytic model from among the comparison analytic models based on a quality metric.

Abstract: A computer implemented system for automating the generation of an analytic model includes a processor configured to process a plurality of data sets. Each data set includes values for a plurality of variables. A time-stamping module is configured to derive values for a plurality of elapsed-time variables for each data set, and the plurality of variables and plurality of elapsed-time variables are included in a plurality of model variables. A model generator is configured to create a plurality of comparison analytic models each based on a different subset of model variables. Each comparison analytic model is configured to operate on new data sets associated with current opportunities, and to output a likelihood of successfully closing each current opportunity. A model testing module is configured to select an operational analytic model from among the comparison analytic models based on a quality metric.

Abstract: The present invention envisages a system and method for automating the generation of business decision analytic models. The system uses a plurality of predictor variables stored in a plurality of data sets, to automatically create a business decision analytic model. The system includes a processor configured to process the data sets and determine the total number of records present in each of the data sets and the number of columns containing only numerical values. The processor selects a column containing only numerical values, from a dataset under consideration, and counts the number of unique numerical values in the selected column, and the total number of records present in the selected column. The two counts are compared and the selected column is transformed using a non-linear transformation to obtain a column of transformed values. The transformed values and corresponding time stamps are utilized for the purpose of model generation.

Abstract: The present invention envisages a system and method for automating the generation of business decision analytic models. The system uses a plurality of predictor variables stored in a plurality of data sets, to automatically create a business decision analytic model. The system includes a processor configured to process the data sets and determine the total number of records present in each of the data sets and the number of columns containing only numerical values. The processor selects a column containing only numerical values, from a dataset under consideration, and counts the number of unique numerical values in the selected column, and the total number of records present in the selected column. The two counts are compared and the selected column is transformed using a non-linear transformation to obtain a column of transformed values. The transformed values and corresponding time stamps are utilized for the purpose of model generation.

Abstract: The present invention envisages a system and method for automating the generation of business decision analytic models. The system uses a plurality of predictor variables stored in a plurality of data sets, to automatically create a business decision analytic model. The system includes a processor configured to process the data sets and determine the total number of records present in each of the data sets and the number of columns containing only numerical values. The processor selects a column containing only numerical values, from a dataset under consideration, and counts the number of unique numerical values in the selected column, and the total number of records present in the selected column. The two counts are compared and the selected column is transformed using a non-linear transformation to obtain a column of transformed values. The transformed values and corresponding time stamps are utilized for the purpose of model generation.

Abstract: The present invention envisages a system and method for automating the generation of business decision analytic models. The system uses a plurality of predictor variables stored in a plurality of data sets, to automatically create a business decision analytic model. The system includes a processor configured to process the data sets and determine the total number of records present in each of the data sets and the number of columns containing only numerical values. The processor selects a column containing only numerical values, from a dataset under consideration, and counts the number of unique numerical values in the selected column, and the total number of records present in the selected column. The two counts are compared and the selected column is transformed using a non-linear transformation to obtain a column of transformed values. The transformed values and corresponding time stamps are utilized for the purpose of model generation.

Abstract: Methods, systems, and apparatus, including computer program products, for detecting and estimating lost sales. A demand distribution for a product provided by a retail presence is determined. A probability of a lost sales occurrence is evaluated, including determining a predetermined time period and a probability of no sales over the predetermined time period. A determination of whether no sales have occurred over a time period corresponding in length to the predetermined time period is made. If the probability of no sales is below a threshold, an estimate of lost sales is determined.

Abstract: Methods and systems for predicting out-of-stock occurrences and for assigning root causes to out-of-stock occurrences are described. In one implementation, inventory data and point of sale data are collected. An expected lost sales value is determined. A true demand is determined based on the point-of-sale data and the expected lost sales value. A probability of an out-of-stock occurrence is determined based on the inventory data. In another implementation, an out-of-stock occurrence is identified. The out-of-stock occurrence is classified, and one or more root causes are assigned to the out-of-stock occurrence.