A framework for adapting online prediction algorithms to outlier detection over time series
Iturria A., Labaien J., Charramendieta S., Lojo A., Del Ser J., Herrera F.
This study introduces a novel framework that eases the adoption of any online prediction algorithm for outlier detection over time series data. The proposed framework comprises both streaming data normalization and online anomaly scoring and identification based on prediction errors. To demonstrate the utility of the proposed framework, a novel neural-network-based online time series anomaly detection algorithm called EORELM-AD is developed by implementing the steps of the proposed framework over an ensemble of online recurrent extreme learning machines. Extensive experiments on well-known benchmark datasets for time series outlier detection are presented and discussed, yielding two main conclusions. First, the performance of the proposed EORELM-AD detector is competitive in comparison to several state-of-the-art outlier detection algorithms. Second, the proposed framework is a useful tool for adapting an online time series prediction algorithm to outlier detection.