Systems and methods for indicator identification

A method for data processing, comprising:

• identifying, by a data component, target time series data for a target metric and candidate time series data for a plurality of indicators predictive of the target metric;
• training, by a training component, a machine learning model to predict the target time series data based on the candidate time series data;
• identifying, by a prediction component, a plurality of indicator pairs including a first pair comprising a source indicator from the plurality of indicators and the target metric, a second pair comprising an intermediate indicator from the plurality of indicators and the target metric, and a third pair comprising the source indicator and the intermediate indicator;
• identifying, by the prediction component, a first time horizon for the source indicator from the plurality of indicators, wherein the source indicator is predictive of the target metric at the first time horizon;
• identifying, by the prediction component, a second time horizon for the intermediate indicator from the plurality of indicators, wherein the intermediate indicator is predictive of the target metric at the second time horizon;
• identifying, by the prediction component, a third time horizon for the source indicator, wherein the source indicator is predictive of the intermediate indicator at the third time horizon;
• and
• generating, by a graph component, a graph including the target metric and the plurality of indicators based on the first time horizon, the second time horizon, and the third time horizon, wherein the graph includes a first edge between the source indicator and the target metric, a second edge between the intermediate indicator and the target metric, and a third edge between the source indicator and the intermediate indicator.

An apparatus for data processing, comprising:

• a processor;
• a memory storing instructions executable by the processor;
• a data component executable by the processor to identify target time series data for a target metric and candidate time series data for a plurality of indicators predictive of the target metric;
• a training component executable by the processor to train a machine learning model to predict the target time series data based on the candidate time series data;
• a prediction component executable by the processor to;
• identify a plurality of indicator pairs including a first pair comprising a source indicator from the plurality of indicators and the target metric, a second pair comprising an intermediate indicator from the plurality of indicators and the target metric, and a third pair comprising the source indicator and the intermediate indicator, and
• compute a first predictivity value for the first pair, a second predictivity value for the second pair, and a third predictivity value for the third pair based on the machine learning model and the plurality of indicator pairs, wherein the first predictivity value indicates that the source indicator from the plurality of indicators is predictive of the target metric, wherein the second predictivity value indicates that the intermediate indicator from the plurality of indicators is predictive of the target metric, and wherein the third predictivity value indicates that the source indicator is predictive of the intermediate indicator;
• and
• a graph component configured to generate a graph including the target metric and the plurality of indicators based on the first predictivity value, the second predictivity value, and the third predictivity value, wherein the graph includes a first edge between the source indicator and the target metric, a second edge between the intermediate indicator and the target metric, and a third edge between the source indicator and the intermediate indicator.