Deep Koopman Predictors for Anomaly Detection of Complex IOT Systems With Time Series Data

Anomaly detection plays an important role in ensuring the safety and reliability of complex Internet of Things (IOT) systems through analysis of time-series data. Generally, real-world signals exhibit intrinsic non-stationary characteristics, resulting in a significant challenge the context of anomaly detection for time-series data. In this study, a novel Wavelet-Koopman predictor is proposed for time-series data modeling and anomaly detection underlying non-stationary dynamics of complex systems. The proposed wavelet-Koopman decomposes non-stationary data into time variant and time invariant components and processes them separately using different neural koopman operators. The operation of Wavelet decomposition avoids the impact of non-stationary temporal data on prediction results so the proposed Wavelet-Koopman is able to model and predict non-stationary time series data, which lays the foundation for the implementation of anomaly detection. Several experiments are conducted on common datasets and anomaly detection of liquid rocket engines are realized. The experimental results demonstrate that the proposed method outperforms several recent methods.