Anomaly Detection in Fractal Time Series with LSTM Autoencoders
Lyudmyla Kirichenko,
Yulia Koval,
Sergiy Yakovlev
et al.
Abstract: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 analysis of the original time series to the analysis of the sequence of Hurst exponent estimates. To this end, we employ an LSTM autoencoder neural network, demonstrating its effectiveness in detecting anomalies wit… Show more
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