... 109, https://doi.org/10.1007/978-3-030-76352-7_14 2021. Fig. 1. Latency metric monitoring with temporal aggregation using different. Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models 1 Introduction.

... scale : The case for deep distributional time series models ' , CORR , abs / 2007.15541 , ( 2020 ) . [ 5 ] Shaojie Bai , J. Zico Kolter , and Vladlen Koltun , ' An empirical eval- uation of generic ... Time Series Anomaly Detection.

While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets.

While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets.

This book tackles important problems of anomaly detection and health status analysis in complex core router systems, integral to today’s Internet Protocol (IP) networks.

By the end of this deep learning book, you'll be able to build a variety of deep learning XAI models and perform validation to assess their explainability.

This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view.

This book will primarily target practitioners and researchers who are newcomers to the area of modern anomaly detection techniques. Advanced-level students in computer science will also find this book helpful with their studies.

This book constitutes revised and selected papers from the scientific satellite events held in conjunction with the18th International Conference on Service-Oriented Computing, ICSOC 2020.

This book constitutes the proceedings of the 18th International Conference on Service-Oriented Computing, ICSOC 2020, which was planned to take place in Dubai, UAE, during December 14-17, 2020.