Multi-perspective anomaly prediction using neural networks

In this paper, we introduce a technique for predicting anomalies in a signal by observing relationships between multiple meaningful transformations of the signal called perspectives. In particular, we use the Fourier transform to provide a holistic view of the frequencies present in a signal, along with a wavelet denoised signal that is filtered to locate anomalous peaks. Then we input these perspectives of the signal into a feedforward neural network technique to recognize patterns in the relationship between perspectives, and the presence of anomalies. The neural network is trained using a supervised learning algorithm for a given data set. Once trained, the neural network outputs the probability of a significant event occurring later in the signal based on anomalies occurring in the early part of the signal. A large collection of seismic signals was used in this study to illustrate the underlying methodology. Using this method we were able to achieve 54.7% accuracy in predicting anomalies further in a seismic signal. The techniques we present in this paper, with some refinement, can readily be applied to detect anomalies in seismic, electrocardiogram, electroencephalogram, and other non-stationary signals.