Laboratory Plasmas: Dynamics of Transport Across Sheared Flows

Sánchez, Raúl; Newman, D. E.

doi:10.1007/978-94-024-1229-1_9

Cited by 1 publication

(1 citation statement)

References 24 publications

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“…The generalized Hurst exponents method is a tool to study the scaling properties of the data, and it is a generalization of the approach from Section 4.1 . Note that the generalized Hurst exponent

, contrary to the regular Hurst exponent, depends on a variable q , which means it is associated with the q th order moment of the distribution of the increments [ 69 ].…”

Section: Complexity Measuresmentioning

confidence: 99%

Combining Measures of Signal Complexity and Machine Learning for Time Series Analyis: A Review

Raubitzek

Neubauer

2021

Entropy

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Measures of signal complexity, such as the Hurst exponent, the fractal dimension, and the Spectrum of Lyapunov exponents, are used in time series analysis to give estimates on persistency, anti-persistency, fluctuations and predictability of the data under study. They have proven beneficial when doing time series prediction using machine and deep learning and tell what features may be relevant for predicting time-series and establishing complexity features. Further, the performance of machine learning approaches can be improved, taking into account the complexity of the data under study, e.g., adapting the employed algorithm to the inherent long-term memory of the data. In this article, we provide a review of complexity and entropy measures in combination with machine learning approaches. We give a comprehensive review of relevant publications, suggesting the use of fractal or complexity-measure concepts to improve existing machine or deep learning approaches. Additionally, we evaluate applications of these concepts and examine if they can be helpful in predicting and analyzing time series using machine and deep learning. Finally, we give a list of a total of six ways to combine machine learning and measures of signal complexity as found in the literature.

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, contrary to the regular Hurst exponent, depends on a variable q , which means it is associated with the q th order moment of the distribution of the increments [ 69 ].…”

Section: Complexity Measuresmentioning

confidence: 99%

Combining Measures of Signal Complexity and Machine Learning for Time Series Analyis: A Review

Raubitzek

Neubauer

2021

Entropy

View full text Add to dashboard Cite

show abstract

Laboratory Plasmas: Dynamics of Transport Across Sheared Flows

Cited by 1 publication

References 24 publications

Combining Measures of Signal Complexity and Machine Learning for Time Series Analyis: A Review

Combining Measures of Signal Complexity and Machine Learning for Time Series Analyis: A Review

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