International Business Machines Corporation

Yea Jane Chu

Jing-Yun Shyr

Sier Han

Jing James Xu

Damir Spisic

Weicai Zhong

Dong Liang

Xueying Zhang

Graham John Wills

Fan Li

Marc S Altshuller

Michael D Woods

A method, system, and/or computer program product analyses event transactional related data to generate insights and predictions, which are pre-created to efficiently respond to requests for prediction/forecasting information, in order to improve the operation of the prediction-generating computer. One or more processors receive a series of structured data, where each entry (E) from the series of structured data has one or more time fields Tand one or more attributes A. In response to determining that the series of structured data is transactional, one or more processors select a time field Tthat meets an aggregation criterion, and then aggregate the transactional data from the time field Tinto a time series data format. One or more processors consolidate results from a time series analysis and a regression analysis of the transformed transactional data to create a consolidated result, which is used to respond to a request for prediction/forecasting information.

Transforming a transactional data set to generate forecasting and prediction insights

Techniques are described for automatic interval metadata determination for intermittent time series data. In one example, a method for determining intermittent time series interval metadata includes detecting one or more time variables in a time series data set. The method further includes determining whether the one or more time variables are intermittently regular. The method further includes determining one or more respective time intervals for the one or more time variables. The method further includes determining the parameters of intermittency for the one or more time variables. The method further includes generating an output comprising information about the one or more time variables based on the one or more respective time intervals and the parameters of intermittency for the time variable.

Automatic time interval metadata determination for business intelligence and predictive analytics

Techniques are described for automated selection of components for a generalized linear model. In one example, a method includes determining a candidate set of distributions, a candidate set of link functions, and a candidate set of predictor variables, based at least in part on a dataset of interest. The method further includes selecting a distribution from the initial candidate set of distributions and a link function from the initial candidate set of link functions, based at least in part on the candidate set of predictor variables; and selecting predictor variables from the candidate set of predictor variables, based at least in part on the selected distribution and the selected link function. The method further includes reiterating the selecting processes until a stopping criterion is fulfilled, and generating a generalized linear model output comprising the selected distribution, the selected link function, and the selected predictor variables.

Automated selection of generalized linear model components for business intelligence analytics

Techniques are described for generating characterizations of time series data. In one example, a method includes extracting a trend-cycle component, a seasonal component, and an irregular component from a time series of data. The method further includes performing one or more pattern analyzes on the trend-cycle component, the seasonal component, and the irregular component. The method further includes, for each pattern analysis of the one or more pattern analyzes, performing a comparison of an analytic result of the respective pattern analysis to a selected significance threshold for the respective pattern analysis to determine if the analytic result passes the significance threshold for the respective pattern analysis. The method further includes generating an output for each of the analytic results that pass the significance threshold for the respective pattern analysis.

Automatic time series exploration for business intelligence analytics

Techniques are described for generating characterizations of time series data. In one example, a method includes extracting a trend-cycle component, a seasonal component, and an irregular component from a time series of data. The method further includes performing one or more pattern analyses on the trend-cycle component, the seasonal component, and the irregular component. The method further includes, for each pattern analysis of the one or more pattern analyses, performing a comparison of an analytic result of the respective pattern analysis to a selected significance threshold for the respective pattern analysis to determine if the analytic result passes the significance threshold for the respective pattern analysis. The method further includes generating an output for each of the analytic results that pass the significance threshold for the respective pattern analysis.

Provided are techniques for imputing a missing value for each of one or more predictor variables. Data is received from one or more data sources. For each of the one or more predictor variables, an imputation model is built based on information of a target variable; a type of imputation model to construct is determined based on the one or more data sources, a measurement level of the predictor variable, and a measurement level of the target variable; and the determined type of imputation model is constructed using basic statistics of the predictor variable and the target variable. The missing value is imputed for each of the one or more predictor variables using the data from the one or more data sources and one or more built imputation models to generate a completed data set.

Missing value imputation for predictive models

Provided are techniques for interaction detection for generalized linear models. Basic statistics are calculated for a pair of categorical predictor variables and a target variable from a dataset during a single pass over the dataset. It is determined whether there is a significant interaction effect for the pair of categorical predictor variables on the target variable by: calculating a log-likelihood value for a full generalized linear model without estimating model parameters; calculating the model parameters for a reduced generalized linear model with a recursive marginal mean accumulation technique using the basic statistics; calculating a log-likelihood value for the reduced generalized linear model; calculating a likelihood ratio test statistic using the log-likelihood value for the full generalized linear model and the log-likelihood value for the reduced generalized linear model; calculating a p-value of the likelihood ratio test statistic; and comparing the p-value to a significance level.

Interaction detection for generalized linear models for a purchase decision

Provided are techniques for computing a task result. A processing data set of records is created, wherein each of the records contains data specific to a sub-task from a set of actual sub-tasks and contains a reference to data shared by the set of actual sub-tasks, and wherein a number of the records is equivalent to a number of the actual sub-tasks in the set of actual sub-tasks. With each mapper in a set of mappers, one of the records of the processing data set is received and an assigned sub-task is executed using the received one of the records to generate output. With a single reducer, the output from each mapper in the set of mappers is reduced to determine a task result.

Computing regression models

A subset of (k&#x2212;1)-dimensional tables are received, wherein k is greater than 1. A set of k-dimensional tables is created by combining each of the (k&#x2212;1)-dimensional tables with a non-included dimension corresponding to a 1-dimensional table. Significance of interaction and interaction effect size is computed for the created set of k-dimensional tables to determine dimension and measure interactions.

Relationship discovery in business analytics

Provided are techniques for generating order statistics and error bounds. For each of multiple, distributed data sources, a finite number of data bins are created for each field in that data source. Data values in each of the multiple, distributed data sources are processed to generate basic summaries for each of the data bins in a single pass of the data values. The data bins from each of the multiple, distributed data sources are sorted. One or more approximate order statistics are computed for a data set by accumulating counts from a number of the sorted data bins. Lower and upper error bounds are provided for each of the computed one or more approximate order statistics, wherein the lower and upper error bounds are values delimiting an interval containing a true value of an order statistic.

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Chicago, IL, United States of America

Yea Jane Chu