Predictive model generation using features

One or more non-transitory computer-readable storage media having instructions stored thereon, wherein the instructions, when executed by one or more processors, cause the one or more processors to perform a computer-implemented method comprising:

• obtaining a set of data points corresponding with a sequence of time series values, of a first time series, determined from machine data;
• adding at least one feature to at least one data point of the set of data points corresponding with the sequence of time series values, wherein the at least one feature captures a relationship between a plurality of time series;
• and
• training a predictive model using the set of data points associated with the first time series, including the at least one feature added to the at least one data point, to generate predicted values associated with a second time series.

A computer-implemented system comprising:

• one or more hardware processors;
• one or more computer-readable storage media having instructions stored thereon, wherein the instructions, when executed by the one or more processors, cause the one or more processors to perform a method comprising:
• obtaining a set of data points corresponding with a sequence of time series values, of a first time series, determined from machine data;
• adding at least one feature to at least one data point of the set of data points corresponding with the sequence of time series values, wherein the at least one feature captures a relationship between a plurality of time series;
• and
• training a predictive model using the set of data points associated with the first time series, including the at least one feature added to the at least one data point, to generate predicted values associated with a second time series.