Feature trajectory reduction of integrated autoregressive processes based on a multilayer self-organizing neural network

A method for reducing the parameters of time variant integrated autoregressive (IAR) processes by means of a multilayer self-organizing neural network is presented. For the test of signal detectors, observed time series are described by a time variant integrated AR-model. The characterizing parameter vector, called feature vector, is continuously calculated with every new observed time sample. The corresponding sequence of feature vectors results in the feature trajectory, which initializes an adaptive filter for generating new time series. We reduce feature trajectories by means of a multilayer self-organizing feature map. The presented network enables the mapping of each feature trajectory into a scalar sequence after optimizing the internal states of each map with an appropriate learning algorithm. Inverse mapping of each scalar sequence into a quantized trajectory shows good reproduction properties. The results show good tracking behavior and an acceptable data reduction which is asymptotically limited by the feature vector dimension. Experimental results are presented for time series corresponding to fire signals.