Providing predictive data predicting data values for a historical dataset. The method facilitates improving the accuracy of the predictive data by identifying for a user, and allowing the user to select ancillary datasets that can be evaluated, using a predictive evaluation, together with a historical dataset to improve the accuracy of the predictive data. A user interface is provided to a user. The user interface identifies one or more ancillary datasets. The ancillary datasets are identified to the user based on the ancillary datasets meeting a threshold condition to a historical dataset. The ancillary datasets are selectable by the user in the user interface. User input is received at the user interface selecting one or more of the ancillary datasets. A predictive dataset is displayed to the user. The predictive dataset is determined by predictive evaluation of the historical dataset and the one or more selected ancillary datasets.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. In a computing environment, a method of providing predictive data predicting data values for a historical dataset, the predictive data predicting either future values for the historical data set or past values for the historical dataset, the method further facilitating improving the accuracy of the predictive data by identifying for a user, and allowing the user to select ancillary datasets that can be evaluated, using a predictive evaluation, together with a historical dataset to improve the accuracy of the predictive data, the method comprising: providing a user interface to a user, the user interface identifying one or more ancillary datasets, the ancillary datasets being identified to the user based on the ancillary datasets meeting a threshold condition to a historical dataset, the ancillary datasets being selectable by the user in the user interface; receiving user input at the user interface selecting one or more of the ancillary datasets; and displaying a predictive dataset to the user, the predictive dataset predicting either future data values or past data values for the historical dataset, the predictive dataset being determined by predictive evaluation of the historical dataset and the one or more selected ancillary datasets. 2. The method of claim 1, further comprising aligning at least one of the one or more ancillary datasets with the historical dataset by modifying a data value interval of the at least one of the one or more ancillary datasets to match a data value interval of the historical dataset. 3. The method of claim 1, wherein at least one threshold condition is based on a time shifted version, shifted in time with respect to the historical dataset, of an ancillary dataset. 4. The method of claim 1, wherein identifying one or more ancillary datasets comprises identifying a plurality of datasets where the datasets in the plurality of datasets are based on a same value set, but based on a different time line. 5. The method of claim 1, wherein identifying one or more ancillary datasets comprises identifying a plurality of datasets where the datasets in the plurality of datasets are different versions of the same dataset based on different aggregation methods. 6. The method of claim 1, further comprising iteratively identifying additional ancillary datasets and providing indications of the additional ancillary datasets in the user interface allowing the user to select the additional ancillary datasets and updating the predicted dataset when an additional ancillary dataset is selected by a user. 7. The method of claim 4, wherein identifying additional ancillary datasets is performed by identifying additional ancillary datasets having high correlation with already identified ancillary datasets. 8. The method of claim 1, further comprising filtering ancillary datasets with a high probability of random correlation to the historical dataset to prevent seemingly relevant ancillary datasets from being identified to the user when they are in fact, irrelevant. 9. A physical computer readable storage device having computer executable instructions stored thereon that when executed by one or more processors causes the following to be performed: providing a user interface to a user, the user interface identifying one or more ancillary datasets, the ancillary datasets being identified to the user based on the ancillary datasets meeting a threshold condition to a historical dataset, the ancillary datasets being selectable by the user in the user interface; receiving user input at the user interface selecting one or more of the ancillary datasets; and displaying a predictive dataset to the user, the predictive dataset predicting either future data values or past data values for the historical dataset, the predictive dataset being determined by predictive evaluation of the historical dataset and the one or more selected ancillary datasets. 10. The physical computer readable storage device of claim 9, further comprising computer executable instructions stored thereon that when executed by one or more processors causes aligning at least one of the one or more ancillary datasets with the historical dataset by modifying a data value interval of the at least one of the one or more ancillary datasets to match a data value interval of the historical dataset. 11. The physical computer readable storage device of claim 9, wherein at least one threshold condition is based on a time shifted version, shifted in time with respect to the historical dataset, of an ancillary dataset. 12. The physical computer readable storage device of claim 9, wherein identifying one or more ancillary datasets comprises identifying a plurality of datasets where the datasets in the plurality of datasets are based on a same value set, but based on a different time line. 13. The physical computer readable storage device of claim 9, wherein identifying one or more ancillary datasets comprises identifying a plurality of datasets where the datasets in the plurality of datasets are different versions of the same dataset based on different aggregation methods. 14. The physical computer readable storage device of claim 9, further comprising computer executable instructions stored thereon that when executed by one or more processors causes iteratively identifying additional ancillary datasets and providing indications of the additional ancillary datasets in the user interface allowing the user to select the additional ancillary datasets and updating the predicted dataset when an additional ancillary dataset is selected by a user. 15. The physical computer readable storage device of claim 14, wherein identifying additional ancillary datasets is performed by identifying additional ancillary datasets having high correlation with already identified ancillary datasets. 16. The physical computer readable storage device of claim 9, further comprising computer executable instructions stored thereon that when executed by one or more processors causes filtering ancillary datasets with a high probability of random correlation to the historical dataset to prevent seemingly relevant ancillary datasets from being identified to the user when they are in fact, irrelevant. 17. In a computing environment, a system for providing predictive data predicting data values for a historical dataset, the predictive data predicting either future values for the historical data set or past values for the historical dataset, the system further facilitating improving the accuracy of the predictive data by identifying for a user, and allowing the user to select ancillary datasets that can be evaluated, using a predictive evaluation, together with a historical dataset to improve the accuracy of the predictive data, the system comprising one or more processors; and one or more computer readable media, wherein the one or more computer readable media comprise computer executable instructions that when executed by at least one of the one or more processors cause the following to be performed: providing a user interface to a user, the user interface identifying one or more ancillary datasets, the ancillary datasets being identified to the user based on the ancillary datasets meeting a threshold condition to a historical dataset, the ancillary datasets being selectable by the user in the user interface; receiving user input at the user interface selecting one or more of the ancillary datasets; and displaying a predictive dataset to the user, the predictive dataset predicting either future data values or past data values for the historical dataset, the predictive dataset being determined by predictive evaluation of the historical dataset and the one or more selected ancillary datasets. 18. The system of claim 17, wherein at least one threshold condition is based on a time shifted version, shifted in time with respect to the historical dataset, of an ancillary dataset. 19. The system of claim 17, wherein identifying one or more ancillary datasets comprises identifying a plurality of datasets where the datasets in the plurality of datasets are based on a same value set, but based on a different time line. 20. The system of claim 17, wherein identifying one or more ancillary datasets comprises identifying a plurality of datasets where the datasets in the plurality of datasets are different versions of the same dataset based on different aggregation methods. 21. A computer-implemented method performed by one or more processors when executing computer-executable instructions, wherein the computer-implemented method controls an interface in a manner that permits the interface to enhance the ability to more accurately forecast predicted values for either future or past values for a historical dataset, the computer-implemented method comprising: storing in a database a historical dataset from which predicted values may be forecast; storing in the database a plurality of ancillary datasets; the one or more processors identifying one or more ancillary datasets that meet one or more threshold conditions by correlating in one or more ways to the predicted values that may be forecast from the historical dataset; based on meeting the threshold condition, presenting at a user interface at least some of the identified ancillary datasets for selection; based on selection of at least one of the ancillary datasets presented at the user interface for selection, the one or more processors presenting at the user interface a predictive dataset that is derived from a combination of the historical dataset and the selected at least one ancillary dataset; determining a forecast method; and the one or more processors applying the determined forecast method to the predictive dataset to enhance the forecast for the predicted values in a manner the improves the accuracy of the predicted values as compared to forecasting them based solely on the historical dataset.