Scalability of Online Hyperparameter Optimization in Kernel Ridge Regression for Multimodal Time Series Anomaly Detection

Abstract

Abstract: Traditional distance and density-based anomaly detection techniques are unable to detect periodic and seasonality related point anomalies which occur commonly in streaming data, leaving a big gap in time series anomaly detection in the current era of the IoT. To address this problem, we present a novel deep learning-based anomaly detection approach (DeepAnT) for time series data, which is equally applicable to the non-streaming cases. DeepAnT is capable of detecting a wide range of anomalies, i.e., point anomalies, contextual anomalies, and discords in time series data. In contrast to the anom

Research Question

How does online hyperparameter optimization for kernel ridge regression scale when applied to real-time anomaly detection in multimodal time series models?

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