Detecting anomalies in time series data via a deep learning algorithm combining wavelets, neural networks and Hilbert transform

The quest for more efficient real-time detection of anomalies in time series data is critically important in numerous applications and systems ranging from intelligent transportation, structural health monitoring, heart disease, and earthquake prediction. Although the range of application is wide, anomaly detection algorithms are usually domain specific and build on experts’ knowledge. Here a new signal processing algorithm – inspired by the...