Patent attributes
Examples of techniques for anomaly detection in a grade crossing prediction system are disclosed. Aspects include receiving 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, 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 plurality of labelled time series of signal values that are larger than a first threshold and a standard deviation for each labelled time series of signal values in the plurality of labelled time series of signal values, and training a machine learning algorithm utilizing the plurality of features.
