Anomaly Detection in Fractal Time Series with LSTM Autoencoders

Anomaly Detection in Fractal Time Series with LSTM Autoencoders

This study explores the application of neural networks for anomaly detection in time series data exhibiting fractal properties, with a particular focus on changes in the Hurst exponent. The objective is to investigate whether changes in fractal properties can be identified by transitioning from the...

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Bibliographic Details
Main Authors:	Kirichenko, Lyudmyla, Koval, Yulia, Yakovlev, Sergiy, Chumachenko, Dmytro
Other Authors:	Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format:	Article
Published:	Multidisciplinary Digital Publishing Institute 2024
Online Access:	https://hdl.handle.net/1721.1/157317

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