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, making them unsuitable for highly dynamic environments. Because field data are predominantly unlabeled, fully unsupervised anomaly detection has become an operational necessity for practical industrial deployment. We present an enhanced, fully autonomous implementation of the Brain-Inspired Cortical Coding (BIC) framework that effectively eliminates the requirement for manual parameter tuning and labeling. The BIC framework combines adaptive clustering with entropy-oriented cortical coding. Unlike most advanced methods, BIC learns directly from unlabeled data and provides stable performance under varying operational conditions. The evaluation was conducted using synthetic Lorenz attractor signals and real-world artificial intelligence for IT operations (AIOps) key performance indicator (KPI) data characterized by rapidly evolving patterns. The comparative experiments included long short-term memory (LSTM), extreme learning machines (ELM), variational autoencoders (VAE), generative adversarial network (GAN)-based models, Isolation Forest, and statistical baselines. Results show BIC outperforms state-of-the-art models, achieving F1-scores of 0.84 and 0.75 under low- and high-noise Lorenz conditions, and an F1-score of approximately 0.80 on the AIOps KPI dataset across both short and long training regimes. Furthermore, under extended evaluation, BIC maintains stable detection performance, whereas competing models show significant deterioration or require additional retraining. These results demonstrate that BIC provides a robust and scalable solution for anomaly detection in noisy, chaotic, and rapidly evolving time-series environments.