SYSTEM AND METHOD TO GENERATE A USER INTERFACE PRESENTING A PREDICITION OR EXPLANATION OF INPUT DATA HAVING A TIME SERIES DEPENDENCY OR A GEOSPATIAL DEPENDENCY

1 . A system comprising:

one or more processors to:

generate, by a first machine learning model receiving as input one or more first features including one or more input data points having a time dependency relationship, a forecast including one or more forecast data points having the time dependency relationship and positions after the one or more data points;

identify, by a second machine learning model receiving as input the first features, one or more second features having respective impact metrics that satisfy an impact threshold, the impact threshold indicating that the second features modify one or more of the forecast data points;

cause a graphical user interface to present the forecast including one or more of the first features having respective first visual properties corresponding to identifiers of respective ones of the first features;

cause the graphical user interface to present the forecast including one or more of the second features having a second visual property corresponding to an indication that the second features satisfy the impact threshold; and

cause the graphical user interface to modify the forecast including the second features to include an explanation portion including one or more metrics of the second features, the metrics corresponding to respective time points of the time dependency relationship.

2 . The system of claim 1 , the processors to:

cause the graphical user interface to present the forecast including an impact indicator corresponding to a second feature of the second features and that indicates a magnitude of impact of the second feature associated with the explanation portion.

3 . The system of claim 1 , the processors to:

cause the graphical user interface to present one or more portions of a selected feature of the second features, the portions of the selected feature having the second visual property.

4 . The system of claim 1 , the processors to:

cause the graphical user interface to present one or more portions of a selected feature of the second features at one or more time points having a time dependency relationship and corresponding to the forecast.

5 . The system of claim 1 , the first visual properties corresponding to a first brightness, and the second visual property corresponding to a second brightness.

6 . The system of claim 1 , the first visual properties corresponding to a first color, and the second visual property corresponding to a second color.

7 . The system of claim 1 , the processors to:

cause the graphical user interface to present a highlight cursor including a bar a particular time or times.

8 . The system of claim 1 , the impact metrics comprise at least one of a direction of impact, a magnitude of impact, or a type of impact.

9 . A method comprising:

generating, by a first machine learning model receiving as input one or more first features including one or more input data points having a time dependency relationship, a forecast including one or more forecast data points having the time dependency relationship and positions after the one or more data points;

identifying, by a second machine learning model receiving as input the first features, one or more second features having respective impact metrics that satisfy an impact threshold, the impact threshold indicating that the second features modify one or more of the forecast data points;

causing a graphical user interface to present the forecast including one or more of the first features having respective first visual properties corresponding to identifiers of respective ones of the first features;

causing the graphical user interface to present the forecast including one or more of the second features having a second visual property corresponding to an indication that the second features satisfy the impact threshold; and

causing the graphical user interface to modify the forecast including the second features to include an explanation portion including one or more metrics of the second features, the metrics corresponding to respective time points of the time dependency relationship.

10 . The method of claim 9 , further comprising:

causing the graphical user interface to present the forecast including an impact indicator corresponding to a second feature of the second features and that indicates a magnitude of impact of the second feature associated with the explanation portion.

11 . The method of claim 9 , further comprising:

causing the graphical user interface to present one or more portions of a selected feature of the second features, the portions of the selected feature having the second visual property.

12 . The method of claim 9 , further comprising:

causing the graphical user interface to present one or more portions of a selected feature of the second features at one or more time points having a time dependency relationship and corresponding to the forecast.

13 . The method of claim 9 , the first visual properties corresponding to a first brightness, and the second visual property corresponding to a second brightness.

14 . The method of claim 9 , the first visual properties corresponding to a first color, and the second visual property corresponding to a second color.

15 . The method of claim 9 , the processors to:

cause the graphical user interface to present a highlight cursor including a bar a particular time or times.

16 . The method of claim 9 , the impact metrics comprise at least one of a direction of impact, a magnitude of impact, or a type of impact.

17 . A method comprising:

generating, by a graphical user interface, a map including one or more geospatial indicators, the geospatial indicators comprising a feature of training data to a machine learning system;

training, by a machine learning system with input including geospatial indicators restricted to the subset of the geospatial indicators and in response to a selection at the graphical user interface of at least a subset of the geospatial indicators, a first model to generate one or more forecast values respective to one or more features including the feature of the geospatial indicators; and

generating, by the graphical user interface and based on the first model, a forecast value presentation portion including a distribution of data points corresponding to the subset of the geospatial indicators.

18 . The method of claim 17 , further comprising:

training, by a machine learning system with input including the features in response to a selection at the graphical user interface of at least a subset of the geospatial indicators, a second model to generate an impact metric corresponding to one or more of the features and the first model.

19 . The method of claim 18 , further comprising:

generating, by the graphical user interface and based on the second model, one or more impact indicators respectively corresponding to one or more of the features.

20 . The method of claim 17 , further comprising:

generating, by the graphical user interface and based on the first model, a distribution presentation portion including a plurality of distributions of data points respectively corresponding to one or more of the features.