Proposal of Reconstructive Reservoir Computing to Detect Anomaly in Time-series Signals

In this paper, we propose reconstructive reservoir computing (RRC) to detect anomaly in time-series signals. In the RRC, an echo state network (ESN) learns to reconstruct normal input signals fed to its input terminals. Since it fails to reconstruct abnormal signals, RRC can detect anomaly based on its reconstruction error. It is shown for the first time that an ESN reconstructs time-series signals effectively for anomaly detection while we already know that it can realize anomaly detection by forecasting. Experiments demonstrate that the RRC works for anomaly detection effectively. Though forecasting errors are used in conventional methods for anomaly detection working for time-series signals, we find experimentally that the reconstruction method has an advantage in its larger margin between normal and abnormal errors. We also find that a smaller leaking rate enhances the ability of anomaly detection. In general, reservoir computing has merits of fast training and, consequently, less energy consumption. We can also make reservoir computing work with the use of physical phenomena. Utilizing reservoir computing is really meaningful for these aspects which layered or other types of recurrent neural networks do not have. Anomaly detection by RRC will be an important application of micro physical-reservoir devices in the near future.