... 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.

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

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.

The key challenge in this task is that it requires a good representation to capture both temporal and spatial relationships in the time-series data.

This precludes current methods from being used in real-world settings by practitioners who are not machine learning experts. In this thesis, we introduce Orion, a machine learning framework for unsupervised time series anomaly detection.