Time series trend root cause identification

1. A method, comprising:

executing, on a processor of a computing device, instructions that cause the computing device to perform operations, the operations comprising:

identifying a set of data, wherein the set of data is organized into multi-dimensional time series data comprising one or more dimensions of measured elements comprising a first measured element and a second measured element;

identifying an overall trend of the multi-dimensional time series data;

identifying element trends, for the measured elements, comprising a first element trend of the first measured element and a second element trend of the second measured element;

calculating weighted correlations between the element trends of the measured elements and the overall trend,

wherein the weighted correlations comprise:

a first weighted correlation between (i) the first element trend and (ii) the overall trend; and

a second weighted correlation between (i) the second element trend and (ii) the overall trend;

determining aggregate weighted correlations of measured element combinations comprising an aggregate weighted correlation for a combination of (i) the first measured element having the first element trend and (ii) the second measured element having the second element trend;

using the overall trend to evaluate the weighted correlations of the measured elements and the aggregate weighted correlations of the measured element combinations to iteratively identify a set of measured elements having a threshold correlation to the overall trend, wherein the using the overall trend to evaluate the weighted correlations and the aggregate weighted correlations comprises including, in the set of measured elements, the combination of (i) the first measured element having the first element trend and (ii) the second measured element having the second element trend based upon the aggregate weighted correlation for the combination having the threshold correlation to the overall trend;

providing an indication of the set of measured elements, including the combination of (i) the first measured element having the first element trend and (ii) the second measured element having the second element trend, as a root cause of the overall trend, wherein the indication includes a number of top sorted root causes, comprising the root cause, populated in a first interface displayed through a user interface;

transitioning the user interface from displaying the first interface to displaying a second interface populated with information related to a dimension of at least one of the first measured element or the second measured element;

evaluating the indication of the set of measured elements to determine whether the root cause of the overall trend corresponds to an operational issue or a non-operational causation factor;

responsive to determining that the root cause of the overall trend corresponds to the operational issue, generating a resolution request for the operational issue corresponding to the root cause of the overall trend; and

routing the resolution request to an entity for resolution.

2. The method of claim 1 , wherein the transitioning the user interface from displaying the first interface to displaying the second interface is performed responsive to a user interaction with the dimension in the first interface.

3. The method of claim 1 , wherein the information related to the dimension populated in the second interface comprises:

a graph representative of at least some of the multi-dimensional time series data.

4. The method of claim 1 , wherein the evaluating comprises:

sorting measured elements within a dimension, of the one or more dimensions, based upon weighted correlations of the measured elements to create a sorted set.

5. The method of claim 4 , wherein the evaluating comprises:

if a weighted correlation of a measured element is greater than a minimum threshold, adding the measured element into a candidate set.

6. The method of claim 1 , comprising:

sorting root causes within a root cause set, comprising the root cause, to create a sorted root cause set based upon counts of measured elements within the root causes and aggregate weighted correlations of the root causes.

7. The method of claim 6 , comprising:

identifying dimensions within a number of top sorted root causes within the sorted root cause set.

8. The method of claim 7 , comprising:

identifying one or more measured elements within the dimensions as the set of measured elements that are the root cause of the overall trend.

9. The method of claim 7 , wherein the number of top sorted root causes corresponds to a user defined number.

10. The method of claim 1 , wherein the non-operational causation factor is associated with a seasonal factor.

11. A non-transitory machine readable medium having stored thereon processor-executable instructions that when executed cause performance of operations, the operations comprising:

identifying an overall trend of multi-dimensional time series data and element trends for measured elements of dimensions within the multi-dimensional time series data, wherein the measured elements comprise a first measured element and a second measured element, wherein the element trends comprise a first element trend of the first measured element and a second element trend of the second measured element;

calculating weighted correlations between the element trends of the measured elements and the overall trend,

wherein the weighted correlations comprise:

a first weighted correlation between (i) the first element trend and (ii) the overall trend; and

a second weighted correlation between (i) the second element trend and (ii) the overall trend;

determining aggregate weighted correlations of measured element combinations comprising an aggregate weighted correlation for a combination of (i) the first measured element having the first element trend and (ii) the second measured element having the second element trend;

using the overall trend to evaluate the weighted correlations of the measured elements and the aggregate weighted correlations of the measured element combinations to iteratively identify a set of measured elements having a threshold correlation to the overall trend, wherein the using the overall trend to evaluate the weighted correlations and the aggregate weighted correlations comprises including, in the set of measured elements, the combination of (i) the first measured element having the first element trend and (ii) the second measured element having the second element trend based upon the aggregate weighted correlation for the combination having the threshold correlation to the overall trend;

providing an indication of the set of measured elements, including the combination of (i) the first measured element having the first element trend and (ii) the second measured element having the second element trend, as a root cause of the overall trend, wherein the indication includes a number of top sorted root causes, comprising the root cause, populated in a first interface displayed through a user interface; and

transitioning the user interface from displaying the first interface to displaying a second interface populated with information related to a dimension of at least one of the first measured element or the second measured element.

12. The non-transitory machine readable medium of claim 11 , wherein the operations comprise:

populating the user interface with dimensions within which the set of measured elements are contained.

13. The non-transitory machine readable medium of claim 11 , wherein the operations comprise:

populating the user interface with the weighted correlations of the measured elements within the set of measured elements.

14. The non-transitory machine readable medium of claim 11 , wherein the operations comprise:

populating the user interface with a graphical illustration of trend data associated with at least one of the multi-dimensional time series data, a measured element associated with the root cause, or a dimension associated with the root cause.

15. The non-transitory machine readable medium of claim 11 , wherein the operations comprise:

in response to receiving user input through the user interface, transitioning from the second interface to a third interface populated with second trend data associated with a second dimension.

16. The non-transitory machine readable medium of claim 11 , wherein the operations comprise:

receiving user input specifying a number of measured elements to consider as the root cause of the overall trend; and

constraining a number of measured elements within the set of measured elements based upon the user input.

17. The non-transitory machine readable medium of claim 11 , wherein the operations comprise:

receiving user input specifying a number of dimensions to consider as the root cause of the overall trend; and

constraining the set of measured elements to comprise measured elements within the number of dimensions based upon the user input.

18. A computing device comprising:

a processor; and

memory comprising processor-executable instructions that when executed by the processor cause performance of operations, the operations comprising:

identifying an overall trend of multi-dimensional time series data and element trends for measured elements of dimensions within the multi-dimensional time series data, wherein the multi-dimensional time series data corresponds to at least one of user interaction data, system diagnostic data, manufacturing data, software service data, or cloud computing operation data, wherein the measured elements comprise a first measured element and a second measured element, wherein the element trends comprise a first element trend of the first measured element and a second element trend of the second measured element;

calculating weighted correlations between the element trends of the measured elements and the overall trend,

wherein the weighted correlations comprise:

a first weighted correlation between (i) the first element trend and (ii) the overall trend; and

a second weighted correlation between (i) the second element trend and (ii) the overall trend;

determining aggregate weighted correlations of measured element combinations comprising an aggregate weighted correlation for a combination of (i) the first measured element having the first element trend and (ii) the second measured element having the second element trend;

using the overall trend to evaluate the weighted correlations of the measured elements and the aggregate weighted correlations of the measured element combinations to iteratively identify a set of measured elements having a threshold correlation to the overall trend, wherein the using the overall trend to evaluate the weighted correlations and the aggregate weighted correlations comprises including, in the set of measured elements, the combination of (i) the first measured element having the first element trend and (ii) the second measured element having the second element trend based upon the aggregate weighted correlation for the combination having the threshold correlation to the overall trend;

providing an indication of the set of measured elements including the combination of (i) the first measured element having the first element trend and (ii) the second measured element having the second element trend, wherein the indication includes a number of top sorted root causes populated in a first interface displayed through a user interface; and

transitioning the user interface from displaying the first interface to displaying a second interface populated with information related to a dimension of at least one of the first measured element or the second measured element.

19. The computing device of claim 18 , wherein the operations comprise:

receiving user input specifying a number of measured elements to consider as a root cause of the overall trend; and

constraining a number of measured elements within the set of measured elements based upon the user input.

20. The computing device of claim 18 , wherein the operations comprise:

receiving user input specifying a number of dimensions to consider as a root cause of the overall trend; and

constraining the set of measured elements to comprise measured elements within the number of dimensions based upon the user input.