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.

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.

You'll learn how multilayered long short-term memory (LSTM) encodes the input time series and a deep LSTM decodes. In anomaly detection, the output is married with “traditional” statistical approaches for anomaly detection.

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.

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. ​