Unsupervised Anomaly Detection in Chaotic Time Series Using Brain-Inspired Cortical Coding
Reliable anomaly detection in time series is crucial yet challenging owing to the chaotic nature of signals from sources such as financial markets or natural systems. However, most state-of-the-art approaches rely heavily on extensive parameter tuning and large labeled datasets, which are rarely available in production...