... 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.

... Series Models Fadhel Ayed1( B ), Lorenzo Stella2, Tim Januschowski2, and Jan Gasthaus2 2 1 University of Oxford, Oxford, UK Amazon ... Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models 1 Introduction.

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

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 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 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.

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 ...

This volume offers an overview of current efforts to deal with dataset and covariate shift.

This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation.