Combining Measures of Signal Complexity and Machine Learning for Time Series Analyis: A Review
Measures of signal complexity, such as the <i>Hurst exponent</i>, the <i>fractal dimension</i>, and the <i>Spectrum of Lyapunov exponents</i>, are used in time series analysis to give estimates on persistency, anti-persistency, fluctuations and predictability of t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/12/1672 |
| _version_ | 1797504928947634176 |
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| author | Sebastian Raubitzek Thomas Neubauer |
| author_facet | Sebastian Raubitzek Thomas Neubauer |
| author_sort | Sebastian Raubitzek |
| collection | DOAJ |
| description | Measures of signal complexity, such as the <i>Hurst exponent</i>, the <i>fractal dimension</i>, and the <i>Spectrum of Lyapunov exponents</i>, 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. |
| first_indexed | 2024-03-10T04:11:23Z |
| format | Article |
| id | doaj.art-a62dd742dae94b50a3db749beb628528 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T04:11:23Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-a62dd742dae94b50a3db749beb6285282023-11-23T08:11:28ZengMDPI AGEntropy1099-43002021-12-012312167210.3390/e23121672Combining Measures of Signal Complexity and Machine Learning for Time Series Analyis: A ReviewSebastian Raubitzek0Thomas Neubauer1Information and Software Engineering Group, Institute of Information Systems Engineering, Faculty of Informatics, TU Wien, Favoritenstrasse 9-11/194, 1040 Vienna, AustriaInformation and Software Engineering Group, Institute of Information Systems Engineering, Faculty of Informatics, TU Wien, Favoritenstrasse 9-11/194, 1040 Vienna, AustriaMeasures of signal complexity, such as the <i>Hurst exponent</i>, the <i>fractal dimension</i>, and the <i>Spectrum of Lyapunov exponents</i>, 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.https://www.mdpi.com/1099-4300/23/12/1672hurst exponentchaosLyapunov exponentsneural networkstime series predictiondeep learning |
| spellingShingle | Sebastian Raubitzek Thomas Neubauer Combining Measures of Signal Complexity and Machine Learning for Time Series Analyis: A Review Entropy hurst exponent chaos Lyapunov exponents neural networks time series prediction deep learning |
| title | Combining Measures of Signal Complexity and Machine Learning for Time Series Analyis: A Review |
| title_full | Combining Measures of Signal Complexity and Machine Learning for Time Series Analyis: A Review |
| title_fullStr | Combining Measures of Signal Complexity and Machine Learning for Time Series Analyis: A Review |
| title_full_unstemmed | Combining Measures of Signal Complexity and Machine Learning for Time Series Analyis: A Review |
| title_short | Combining Measures of Signal Complexity and Machine Learning for Time Series Analyis: A Review |
| title_sort | combining measures of signal complexity and machine learning for time series analyis a review |
| topic | hurst exponent chaos Lyapunov exponents neural networks time series prediction deep learning |
| url | https://www.mdpi.com/1099-4300/23/12/1672 |
| work_keys_str_mv | AT sebastianraubitzek combiningmeasuresofsignalcomplexityandmachinelearningfortimeseriesanalyisareview AT thomasneubauer combiningmeasuresofsignalcomplexityandmachinelearningfortimeseriesanalyisareview |