... 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.
... 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.
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 discusses a variety of methods for outlier ensembles and organizes them by the specific principles with which accuracy improvements are achieved.
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.
The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.
With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design ...