Disclosed are methods, system, and computer program products useful for generating summary statistics for data predictions based on the aggregation of data from past time intervals. Summary statistics such as prediction standard errors, variances, confidence limits, and other statistical measures, may be generated in a way that preserves the basic distributional properties of the original data sets, to allow, for example, a reduction of the multiple data sets through the aggregation process, which may be useful for a prediction process, while determining statistical information for the predicted data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: one or more processors; a non-transitory computer readable storage medium positioned in data communication with the one or more processors and including instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including: identifying a plurality of data sets, wherein each data set includes previous data, modeled data, and one or more data set attributes; receiving a filter criterion for filtering the plurality of data sets based on the data set attributes; filtering the plurality of data sets using the filter criterion to identify a filtered plurality of data sets that is a subset of the plurality of data sets, wherein each filtered data set has one or more data set attributes that are associated with the filter criterion, and wherein a filtered data set includes filtered previous data and filtered modeled data; identifying an aggregation type, wherein the aggregation type identifies how the filtered plurality of data sets are to be aggregated; generating an aggregated data set, wherein generating includes aggregating the filtered plurality of data sets using the aggregation type, wherein the aggregated data set includes an aggregated previous data set and an aggregated modeled data set, wherein the aggregated previous data set is generated using the filtered previous data, and wherein the aggregated modeled data set is generated using the filtered modeled data; generating an aggregate prediction using the aggregated data set; and reconciling the aggregate prediction and the aggregated modeled data set to determine prediction statistics for the aggregate prediction. 2. The system of claim 1, wherein previous data includes a sequence of measured data values made over a previous time interval. 3. The system of claim 1, wherein modeled data includes a sequence of modeled data values made over a previous time interval. 4. The system of claim 3, wherein modeled data includes a sequence of summary statistics associated with the sequence of modeled data values. 5. The system of claim 1, wherein identifying the aggregation type includes receiving input corresponding to determination of the aggregation type. 6. The system of claim 1, wherein aggregating includes forming a single aggregated previous data set from the filtered previous data, or wherein aggregating includes forming a single aggregated modeled data set from the filtered modeled data. 7. The system of claim 1, wherein the aggregate prediction includes predicted data for an upcoming time interval. 8. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause a computing device to perform operations including: identifying, using a hardware processor of the computing device, a plurality of data sets, wherein the plurality of data sets includes previous data, modeled data, and one or more data set attributes; receiving a filter criterion for filtering the plurality of data sets based on the data set attributes; filtering the plurality of data sets using the filter criterion to identify a filtered plurality of data sets that is a subset of the plurality of data sets, wherein each filtered data set has one or more data set attributes that are associated with the filter criterion, and wherein a filtered data set includes filtered previous data and filtered modeled data; identifying an aggregation type, wherein the aggregation type identifies how the filtered plurality of data sets are to be aggregated; generating an aggregated data set, wherein generating includes aggregating the filtered plurality of data sets using the aggregation type, wherein the aggregated data set includes an aggregated previous data set and an aggregated modeled data set, wherein the aggregated previous data set is generated using the filtered previous data, and wherein the aggregated modeled data set is generated using the filtered modeled data; generating an aggregate prediction using the aggregated data set; and reconciling the aggregate prediction and the aggregated modeled data set to determine prediction statistics for the aggregate prediction. 9. The computer program product of claim 8, wherein previous data includes a sequence of measured data values made over a previous time interval. 10. The computer program product of claim 8, wherein modeled data includes a sequence of modeled data values made over a previous time interval. 11. The computer program product of claim 10, wherein modeled data includes a sequence of summary statistics associated with the sequence of modeled data values. 12. The computer program product of claim 8, wherein identifying the aggregation type includes receiving input corresponding to determination of the aggregation type. 13. The computer program product of claim 8, wherein aggregating includes forming a single aggregated previous data set from the filtered previous data or wherein aggregating includes forming a single aggregated modeled data set from the filtered modeled data. 14. The computer program product of claim 8, wherein the aggregate prediction includes predicted data for an upcoming time interval. 15. A computer implemented method for generating an aggregate prediction, the method comprising: identifying, at a computing device, a plurality of data sets, wherein the plurality of data sets includes previous data, modeled data, and one or more data set attributes; receiving a filter criterion for filtering the plurality of data sets based on the data set attributes; filtering the plurality of data sets using the filter criterion to identify a filtered plurality of data sets that is a subset of the plurality of data sets, wherein each filtered data set has one or more data set attributes that are associated with the filter criterion, and wherein a filtered data set includes filtered previous data and filtered modeled data; identifying an aggregation type, wherein the aggregation type identifies how the filtered plurality of data sets are to be aggregated; generating an aggregated data set, wherein generating includes aggregating the filtered plurality of data sets using the aggregation type, wherein the aggregated data set includes an aggregated previous data set and an aggregated modeled data set, wherein the aggregated previous data set is generated using the filtered previous data, and wherein the aggregated modeled data set is generated using the filtered modeled data; generating an aggregate prediction using the aggregated data set; and reconciling the aggregate prediction and the aggregated modeled data set to determine summary statistics for the aggregate prediction. 16. The method of claim 15, wherein previous data includes a sequence of measured data values made over a previous time interval. 17. The method of claim 15, wherein modeled data includes a sequence of modeled data values made over a previous time interval. 18. The method of claim 17, wherein modeled data includes a sequence of summary statistics associated with the sequence of modeled data values. 19. The method of claim 15, wherein identifying the aggregation type includes receiving input corresponding to determination of the aggregation type. 20. The method of claim 15, wherein aggregating includes forming a single aggregated previous data set from the filtered previous data or wherein aggregating includes forming a single aggregated modeled data set from the filtered modeled data. 21. The method of claim 15, wherein the aggregate prediction includes predicted data for an upcoming time interval.