... 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.

Anomalies are generally defined as observations that deviate from the standard, normal or expected values. Specifically, this work is divided into two phases.

In this book, we embark on a journey to explore the principles, methodologies, and applications of data analytics specifically tailored for time series data.

Time series anomaly detection is an important problem in the field of machine learning.

A basic understanding of deep learning and anomaly detection–related topics using Python is recommended to get the most out of this book.

By contrast, this book aims at efficient discovery in time series, rather than prediction, and its novelty lies in its algorithmic contributions and its simple, practical algorithms and case studies.

This book constitutes the refereed proceedings of the 6th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2021, held during September 13-17, 2021.

In this thesis, I explored machine learning and other statistical techniques for anomaly detection on time series data obtained from Internet-of-Things sensors.