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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving, by one or more processors, a series of structured data, wherein each entry (Ei) from the series of structured data has one or more time fields Tk (Ti1, Ti2, . . . , TiK) and one or more attributes Aj (Ai1, Ai2, . . . , AiJ); analyzing, by one or more processors, the series of structured data to determine if the series of structured data is transactional data that describes an operational event; in response to determining that the series of structured data is transactional data that describes an operational event, identifying, by one or more processors, transactional data from the series of structured data; selecting, by one or more processors, a time field Tkr that meets an aggregation criterion; aggregating, by one or more processors, the transactional data from the time field range Tkr into a time series data format; analyzing, by one or more processors, the time series data by a time series analysis to create a forecast, wherein the forecast generates time-dependent forecasts of events, and wherein the time-dependent forecasts of events are based on sequential occurrences of future events; deriving, by one or more processors, time related fields from the time field range Tkr with the one or more attributes Aj into a cross-sectional data format; analyzing, by one or more processors, the cross-sectional data by a regression analysis to form results used in a prediction of the future events, wherein the predictions of the future events are predictions that are based on sequential occurrences of the future events; transforming, by one or more processors, the series of structured data to a cross sectional data format; performing, by one or more processors, a regression analysis on the series of structured data in the cross sectional data format; consolidating, by one or more processors, results from the time series analysis and the regression analysis to create a consolidated result; storing, by one or more processors, the consolidated result in a consolidated result-generating computer; receiving, by one or more processors, a request for prediction information from a requesting computer; and responding, by one or more processors, to the request by sending the consolidated results from the consolidated result-generating computer to the requesting computer. 2. The method of claim 1, wherein the attributes Aj are selected from a group consisting of labels, strings, location, numbers, counts, and statistics for data in the structured data. 3. The method of claim 1, wherein the transactional data is identified as being transactional in nature based on whether the time field is in chronological order for entries in the structured data. 4. The method of claim 1, further comprising: utilizing, by one or more processors, natural language processing (NLP) to determine one or more primary keys in the transactional data; and constructing, by one or more processors, target fields from the primary key, wherein the target fields are used for the time series analysis and the regression analysis. 5. A computer program product comprising one or more computer readable storage mediums, and program instructions stored on at least one of the one or more storage mediums, the stored program instructions comprising: program instructions to receive a series of structured data, wherein each entry (Ei) from the series of structured data has one or more time fields Tk (Ti1, Ti2, . . . , TiK) and one or more attributes Aj (Ai1, Ai2, . . . , AiJ); program instructions to analyze the series of structured data to determine if the series of structured data is transactional data that describes an operational event; program instructions to, in response to determining that the series of structured data is transactional data that describes an operational event, identify transactional data from the series of structured data; program instructions to select a time field range Tkr that meets an aggregation criterion; program instructions to aggregate the transactional data from the time field range Tkr into a time series data format; program instructions to analyze the time series data by a time series analysis to create a forecast, wherein the forecast generates time-dependent forecasts of events, and wherein the time-dependent forecasts of events are based on sequential occurrences of future events; program instructions to derive time related fields from the time field range Tkr with the one or more attributes Aj into a cross-sectional data format; program instructions to analyze the cross-sectional data by regression analysis to form results used in a prediction of the future events, wherein the predictions of the future events are predictions that are based on sequential occurrences of the future events; program instructions to transform the series of structured data to a cross sectional data format; program instructions to perform a regression analysis on the series of structured data in the cross sectional data format; program instructions to consolidate results from the time series analysis and the regression analyses to create a consolidated result; program instructions to receive a request for prediction information from a requesting computer; and program instructions to respond to the request by sending the consolidated results to the requesting computer. 6. The computer program product of claim 5, wherein the attributes Aj are selected from a group consisting of labels, strings, location, numbers, counts, and statistics for data in the structured data. 7. The computer program product of claim 5, wherein the transactional data is identified as being transactional in nature based on whether the time field is in chronological order for entries in the structured data. 8. The computer program product of claim 5, wherein the method further comprises: program instructions to utilize natural language processing (NLP) to determine one or more primary keys in the transactional data; and program instructions to construct target fields from the primary key, wherein the target fields are used for the time series analysis and the regression analysis. 9. A computer system comprising one or more processors, one or more computer readable memories, and one or more computer readable storage mediums, and program instructions stored on at least one of the one or more storage mediums for execution by at least one of the one or more processors via at least one of the one or more memories, the stored program instructions comprising: program instructions to receive a series of structured data, wherein each entry (Ei) from the series of structured data has one or more time fields Tk (Ti1, Ti2, . . . , TiK) and one or more attributes Aj (Ai1, Ai2, . . . , AiJ); program instructions to analyze the series of structured data to determine if the series of structured data is transactional data that describes an operational event program instructions to, in response to determining that the series of structured data is transactional data that describes an operational event, identify transactional data from the series of structured data; program instructions to select a time field range Tkr that meets an aggregation criterion; program instructions to aggregate the transactional data from the time field range Tkr into a time series data format; program instructions to analyze the time series data by a time series analysis to create a forecast, wherein the forecast generates time-dependent forecasts of events, and wherein the time-dependent forecasts of events are based on sequential occurrences of future events; program instructions to derive time related fields from the time field range Tkr with the one or more attributes Aj into a cross-sectional data format; program instructions to analyze the cross-sectional data by regression analysis to form results used in a prediction of the future events, wherein the predictions of the future events are predictions that are based on sequential occurrences of the future events; program instructions to transform the series of structured data to a cross sectional data format; program instructions to perform a regression analysis on the series of structured data in the cross sectional data format; program instructions to consolidate results from the time series analysis and the regression analyses to create a consolidated result; program instructions to receive a request for prediction information from a requesting computer; and program instructions to respond to the request by sending the consolidated results to the requesting computer. 10. The computer system of claim 9, wherein the attributes Aj are selected from a group consisting of labels, strings, location, numbers, counts, and statistics for data in the structured data. 11. The computer system of claim 9, wherein the transactional data is identified as being transactional in nature based on whether the time field is in chronological order for entries in the structured data. 12. The computer system of claim 9, wherein the method further comprises: program instructions to utilize natural language processing (NLP) to determine one or more primary keys in the transactional data; and program instructions to construct target fields from the primary key, wherein the target fields are used for the time series analysis and the regression analysis.