Methods and systems for processing data having varied temporal characteristics to generate predictions related to management arrangements using random forest classifiers

A method of processing data having varied temporal characteristics to generate predictions related to management arrangements using random forest classifiers, the method comprising:

• receiving, using control circuitry, first data related to a first management arrangement of a first entity, wherein the first data comprises a first temporal characteristic and a first management transformation of the first entity;
• generating, using the control circuitry, a first feature vector for the first data, wherein a first element of the first feature vector corresponds to the first temporal characteristic whereby the first feature vector is indexed by time;
• training, using the control circuitry, a temporally unaware random forest classifier in which observations are taken to be independent and identically distributed based on the first feature vector to classify the first data as corresponding to the first management transformation;
• receiving, using the control circuitry, second data related to a second management arrangement of a second entity, wherein the second data comprises a second temporal characteristic and an unknown management transformation of the second entity;
• generating, using the control circuitry, a second feature vector for the second data, wherein a second element of the second feature vector corresponds to the second temporal characteristic whereby the second feature vector is indexed by time;
• inputting, using the control circuitry, the second feature vector into the temporally unaware random forest classifier;
• receiving, using the control circuitry, an output from the temporally unaware random forest classifier related to a predicted second management transformation;
• and
• generating for display, in a user interface, a prediction based on the predicted second management transformation.