Real time interactive prediction

Patent metadata

Field	Value
Publication number	US-10510012-B2
Application number	US-201414262923-A
Country	US
Kind code	B2
Filing date	Apr 28, 2014
Priority date	Apr 28, 2014
Publication date	Dec 17, 2019
Grant date	Dec 17, 2019

Abstract

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.

First claim

What is claimed is:

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:
accessing the historical dataset;
identifying one or more ancillary datasets as being correlated to the historical dataset, each of the one or more ancillary datasets having been identified as meeting or exceeding a correlation threshold when compared to the historical dataset and filtering the ancillary datasets with a probability of random correlation to the historical dataset which exceeds a particular threshold;
displaying a user interface wherein the one or more ancillary datasets are selectably displayed such that the ancillary datasets are selectable by a user through the user interface;
receiving user input at the user interface selecting one or more of the ancillary datasets;
determining a predictive dataset by predictive evaluation of the historical dataset and the one or more selected ancillary datasets chosen by the user; and
displaying the predictive dataset to the user, the predictive dataset predicting either future data values or past data values for the historical dataset.

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 6 , wherein identifying additional ancillary datasets is performed by identifying additional ancillary datasets having a correlation with already identified ancillary datasets which exceeds a particular threshold.

8. A computer program product comprising a computer readable hardware storage device having computer executable instructions stored thereon that when executed by one or more processors causes the following to be performed:
accessing a historical dataset;
identifying one or more ancillary datasets as being correlated to the historical dataset, each of the one or more ancillary datasets having been identified as meeting or exceeding a correlation threshold when compared to the historical dataset and filtering the ancillary datasets with a probability of random correlation to the historical dataset which exceeds a particular threshold;
displaying a user interface wherein the one or more ancillary datasets are selectably displayed such that the ancillary datasets are selectable by a user through the user interface;
receiving user input at the user interface selecting one or more of the ancillary datasets;
determining a predictive dataset by predictive evaluation of the historical dataset and the one or more selected ancillary datasets chosen by the user; and
displaying the predictive dataset to the user, the predictive dataset predicting either future data values or past data values for the historical dataset.

9. The computer program product of claim 8 , 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.

10. The computer program product of claim 8 , 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.

11. The computer program product of claim 8 , 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.

12. The computer program product of claim 8 , 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.

13. The computer program product of claim 8 , 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.

14. The computer program product of claim 13 , wherein identifying additional ancillary datasets is performed by identifying additional ancillary datasets having a correlation with already identified ancillary datasets which exceeds a particular threshold.

15. 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:
accessing the historical dataset;
identifying one or more ancillary datasets as being correlated to the historical dataset, each of the one or more ancillary datasets having been identified as meeting or exceeding a correlation threshold when compared to the historical dataset and filtering the ancillary datasets with a probability of random correlation to the historical dataset which exceeds a particular threshold;
displaying a user interface wherein the one or more ancillary datasets are selectably displayed such that the ancillary datasets are selectable by a user through the user interface;
receiving user input at the user

Assignees

• Microsoft Technology Licensing Llc

Inventors

• Netz Amir
• Golan Moshe
• Cheung Chairy Chiu Ying
• Berezansky Yury
• Levy Oded Bar
• Yassour Yoav
• Sagiv Yifat
• Didi Ran

Classifications

• G06N5/048 Primary

  Fuzzy inferencing · CPC title

• G06Q30/0202

  Market predictions or forecasting for commercial activities · CPC title

• G06Q10/04 Primary

  Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) · CPC title