Anomaly detection using machine learning

A system for anomaly detection in a grade crossing prediction system, the system comprising:

• a processor communicatively coupled to a memory, the processor configured to perform:
• receiving, by the processor, a training data set comprising a plurality of labelled time series of signal values from a track circuit in a grade crossing predictor system;
• removing one or more non-unique values from each labelled time series of signal values in the plurality of labelled time series of signal values to create a first set of labelled time series of signals;
• extracting a plurality of features from the plurality of labelled time series of signal values, the plurality of features comprising:
• a number of signal values for each labeled time series of signal values in the first set of labelled time series of signal values that are larger than a first threshold and smaller than a maximum impedance value;
• a standard deviation for each labelled time series of signal values in the first set of labelled time series of signal values;
• training a machine learning algorithm utilizing the plurality of features.

A computer program product for anomaly detection in a grade crossing prediction system, the computer program product being on a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processing device to cause the processing device to perform a method comprising:

• receiving, by the processing device, a training data set comprising a plurality of labelled time series of signal values from a track circuit in a grade crossing predictor system;
• removing one or more non-unique values from each labelled time series of signal values in the plurality of labelled time series of signal values to create a first set of labelled time series of signal values;
• extracting a plurality of features from the plurality of labelled time series of signal values, the plurality of features comprising:
• a number of signal values for each labeled time series of signal values in the first set of labelled time series of signal values that are larger than a first threshold and smaller than a maximum impedance value;
• a standard deviation for each labelled time series of signal values in the first set of labelled time series of signal values;
• training a machine learning algorithm utilizing the plurality of features.