Time-Series Anomaly Detection with Implicit Neural Representation. (arXiv:2201.11950v1 [cs.LG])

Web: http://arxiv.org/abs/2201.11950

Detecting anomalies in multivariate time-series data is essential in many
real-world applications. Recently, various deep learning-based approaches have
shown considerable improvements in time-series anomaly detection. However,
existing methods still have several limitations, such as long training time due
to their complex model designs or costly tuning procedures to find optimal
hyperparameters (e.g., sliding window length) for a given dataset. In our
paper, we propose a novel method called Implicit Neural Representation-based
Anomaly Detection (INRAD). Specifically, we train a simple multi-layer …

anomaly detection arxiv detection neural representation time