An aircraft fault detection system comprising: at least one aircraft data logging device configured to capture parametric flight data from at least one aircraft subsystem; wherein the parametric flight data comprises training data and test data, the training data including subject matter expert identified training variables, obtained when the parametric flight data is captured, for the at least one aircraft subsystem and a flight leg (113A-113n) where a majority of maintenance messages (115AFLM) for the at least one aircraft subsystem occur; the test data including test variables for the at least one aircraft subsystem and the flight leg (113A-113n), the test variables corresponding to the subject matter identified training variables, and an aircraft controller coupled to the data logging device, the aircraft controller being configured to: train, using the training data, a machine learning model to produce a trained machine learning model for forecasting anomalous behavior using the test data; wherein the subject matter expert identified training variables and test variables each including a collection of time-series data (117A, 117B) collected during the flight leg of aircraft in flight, group the subject matter expert identified training variables from the at least one aircraft subsystem into a plurality of training states, group the parametric flight data into a plurality of test states, the plurality of test states, each indicating a state of operation of the aircraft, being determined from the plurality of training states, and one or more of the test states (125A-125n) being different from other test states (125A-125n) in the plurality of test states, generate at least one test transition matrix from the plurality of test states by accumulating the subject matter expert identified test variables into a matrix of test variables; determine the anomalous behavior of the at least one aircraft subsystem by applying the trained machine learning model to the at least one test transition matrix, and forecast faults within the at least one aircraft subsystem based on the anomalous behavior of the at least one aircraft subsystem determined from the at least one test transition matrix, comprising for the anomalous behavior, the aircraft controller identifying the subject matter expert identified test variables and a respective time within the flight leg that the subject matter expert identified test variable was anomalous. at least one aircraft data logging device configured to capture parametric flight data from at least one aircraft subsystem; wherein the parametric flight data comprises training data and test data, the training data including subject matter expert identified training variables, obtained when the parametric flight data is captured, for the at least one aircraft subsystem and a flight leg (113A-113n) where a majority of maintenance messages (115AFLM) for the at least one aircraft subsystem occur; the test data including test variables for the at least one aircraft subsystem and the flight leg (113A-113n), the test variables corresponding to the subject matter identified training variables, and an aircraft controller coupled to the data logging device, the aircraft controller being configured to: train, using the training data, a machine learning model to produce a trained machine learning model for forecasting anomalous behavior using the test data; wherein the subject matter expert identified training variables and test variables each including a collection of time-series data (117A, 117B) collected during the flight leg of aircraft in flight, group the subject matter expert identified training variables from the at least one aircraft subsystem into a plurality of training states, group the parametric flight data into a plurality of test states, the plurality of test states, each indicating a state of operation of the aircraft, being determined from the plurality of training states, and one or more of the test states (125A-125n) being different from other test states (125A-125n) in the plurality of test states, train, using the training data, a machine learning model to produce a trained machine learning model for forecasting anomalous behavior using the test data; wherein the subject matter expert identified training variables and test variables each including a collection of time-series data (117A, 117B) collected during the flight leg of aircraft in flight, group the subject matter expert identified training variables from the at least one aircraft subsystem into a plurality of training states, group the parametric flight data into a plurality of test states, the plurality of test states, each indicating a state of operation of the aircraft, being determined from the plurality of training states, and one or more of the test states (125A-125n) being different from other test states (125A-125n) in the plurality of test states, generate at least one test transition matrix from the plurality of test states by accumulating the subject matter expert identified test variables into a matrix of test variables; determine the anomalous behavior of the at least one aircraft subsystem by applying the trained machine learning model to the at least one test transition matrix, and forecast faults within the at least one aircraft subsystem based on the anomalous behavior of the at least one aircraft subsystem determined from the at least one test transition matrix, comprising for the anomalous behavior, the aircraft controller identifying the subject matter expert identified test variables and a respective time within the flight leg that the subject matter expert identified test variable was anomalous.