Abstract: Embodiments include predicting transactions by an entity and identifying promotions to offer the entity. Aspects include parsing a plurality of event records corresponding to a plurality of entities respectively. Aspects also include identifying a sequence of events corresponding to the entity and discretizing time durations and event values of the sequence of events into discrete symbolic values. Aspects further include generating a temporal pattern of events in the sequence of events, the temporal pattern including a sequence of transaction-symbols representative of the time duration and the event value of the events in the sequence of events of the entity and predicting a next transaction based on the temporal pattern.

Abstract: Embodiments include predicting transactions by an entity and identifying promotions to offer the entity. Aspects include parsing a plurality of event records corresponding to a plurality of entities respectively. Aspects also include identifying a sequence of events corresponding to the entity and discretizing time intervals and event values of the sequence of events into discrete symbolic values. Aspects further include generating a temporal pattern of events in the sequence of events, the temporal pattern including a sequence of transaction-symbols representative of the time interval and the event value of the events in the sequence of events of the entity and predicting a next transaction based on the temporal pattern.

Abstract: 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 (Ei) from the series of structured data has one or more time fields Tk and one or more attributes Aj. In response to determining that the series of structured data is transactional, one or more processors select a time field Tkr that meets an aggregation criterion, and then aggregate the transactional data from the time field Tkr into 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.

Abstract: A computer-implemented method for prescriptive time-series forecasting, which combines both what-if analysis and goal-seeking analysis. The method comprises building a model for a target metric with a set of predictors, based on historical time-series data, and computing, using the model, a set of forecast values. Using the set of forecast values with respect to a forecasting period, both a set of goals for the target metric and a set of constraints for the predictors are analyzed. A set of updated forecasts based on the analyses with respect to the forecasting period is determined to meet the goals within the set of constraints. The updated set of forecasts is presented with respect to the forecasting period, e.g., using a table, a visualization, and/or an interactive user interface.

Abstract: A computer-implemented method for prescriptive time-series forecasting, which combines both what-if analysis and goal-seeking analysis. The method comprises building a model for a target metric with a set of predictors, based on historical time-series data, and computing, using the model, a set of forecast values. Using the set of forecast values with respect to a forecasting period, both a set of goals for the target metric and a set of constraints for the predictors are analyzed. A set of updated forecasts based on the analyses with respect to the forecasting period is determined to meet the goals within the set of constraints. The updated set of forecasts is presented with respect to the forecasting period, e.g., using a table, a visualization, and/or an interactive user interface.

Abstract: 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.

Abstract: 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.

Abstract: Approaches presented herein enable change detection in a data set underlying a time-dependent visualization by comparing annotation snapshots across time. More specifically, a plurality of annotation snapshots of an annotation generated based on an underlying data set of a time-dependent visualization are obtained at a plurality of points in time. These annotation snapshots are monitored for indicia of a pattern change over time against a predetermined reference point. Whether there has been a pattern change is determined based on the monitoring and, in response to detection of a pattern change, an alert is generated. If there has not been a pattern change, the annotation snapshots are monitored for indicia of an anomaly change over time against the predetermined reference point. Whether there has been an anomaly change is determined based on this monitoring and, in response to detection of an anomaly change, an alert is generated.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A computer-implemented method for prescriptive time-series forecasting, which combines both what-if analysis and goal-seeking analysis. The method comprises building a model for a target metric with a set of predictors, based on historical time-series data, and computing, using the model, a set of forecast values. Using the set of forecast values with respect to a forecasting period, both a set of goals for the target metric and a set of constraints for the predictors are analyzed. A set of updated forecasts based on the analyses with respect to the forecasting period is determined to meet the goals within the set of constraints. The updated set of forecasts is presented with respect to the forecasting period, e.g., using a table, a visualization, and/or an interactive user interface.

Abstract: A computer-implemented method for prescriptive time-series forecasting, which combines both what-if analysis and goal-seeking analysis. The method comprises building a model for a target metric with a set of predictors, based on historical time-series data, and computing, using the model, a set of forecast values. Using the set of forecast values with respect to a forecasting period, both a set of goals for the target metric and a set of constraints for the predictors are analyzed. A set of updated forecasts based on the analyses with respect to the forecasting period is determined to meet the goals within the set of constraints. The updated set of forecasts is presented with respect to the forecasting period, e.g., using a table, a visualization, and/or an interactive user interface.

Abstract: 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 (Ei) from the series of structured data has one or more time fields Tk and one or more attributes Aj. In response to determining that the series of structured data is transactional, one or more processors select a time field Tkr that meets an aggregation criterion, and then aggregate the transactional data from the time field Tkr into 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.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments include predicting transactions by an entity and identifying promotions to offer the entity. Aspects include parsing a plurality of event records corresponding to a plurality of entities respectively. Aspects also include identifying a sequence of events corresponding to the entity and discretizing time intervals and event values of the sequence of events into discrete symbolic values. Aspects further include generating a temporal pattern of events in the sequence of events, the temporal pattern including a sequence of transaction-symbols representative of the time interval and the event value of the events in the sequence of events of the entity and predicting a next transaction based on the temporal pattern.

Abstract: Embodiments include predicting transactions by an entity and identifying promotions to offer the entity. Aspects include parsing a plurality of event records corresponding to a plurality of entities respectively. Aspects also include identifying a sequence of events corresponding to the entity and discretizing time durations and event values of the sequence of events into discrete symbolic values. Aspects further include generating a temporal pattern of events in the sequence of events, the temporal pattern including a sequence of transaction-symbols representative of the time duration and the event value of the events in the sequence of events of the entity and predicting a next transaction based on the temporal pattern.

Abstract: 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.

Abstract: 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.