Considerations for Applying Entropy Methods to Temporally Correlated Stochastic Datasets Article Swipe
Joshua J. Liddy
,
Michael A. Busa
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/e25020306
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/e25020306
The goal of this paper is to highlight considerations and provide recommendations for analytical issues that arise when applying entropy methods, specifically Sample Entropy (SampEn), to temporally correlated stochastic datasets, which are representative of a broad range of biomechanical and physiological variables. To simulate a variety of processes encountered in biomechanical applications, autoregressive fractionally integrated moving averaged (ARFIMA) models were used to produce temporally correlated data spanning the fractional Gaussian noise/fractional Brownian motion model. We then applied ARFIMA modeling and SampEn to the datasets to quantify the temporal correlations and regularity of the simulated datasets. We demonstrate the use of ARFIMA modeling for estimating temporal correlation properties and classifying stochastic datasets as stationary or nonstationary. We then leverage ARFIMA modeling to improve the effectiveness of data cleaning procedures and mitigate the influence of outliers on SampEn estimates. We also emphasize the limitations of SampEn to distinguish among stochastic datasets and suggest the use of complementary measures to better characterize the dynamics of biomechanical variables. Finally, we demonstrate that parameter normalization is not an effective procedure for increasing the interoperability of SampEn estimates, at least not for entirely stochastic datasets.
Related Topics
Concepts
Autoregressive fractionally integrated moving average
Sample entropy
Autoregressive model
Outlier
Computer science
Mathematics
Econometrics
Artificial intelligence
Pattern recognition (psychology)
Long memory
Volatility (finance)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/e25020306
- https://www.mdpi.com/1099-4300/25/2/306/pdf?version=1675922180
- OA Status
- gold
- Cited By
- 2
- References
- 68
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4319599930
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4319599930Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/e25020306Digital Object Identifier
- Title
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Considerations for Applying Entropy Methods to Temporally Correlated Stochastic DatasetsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-02-07Full publication date if available
- Authors
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Joshua J. Liddy, Michael A. BusaList of authors in order
- Landing page
-
https://doi.org/10.3390/e25020306Publisher landing page
- PDF URL
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https://www.mdpi.com/1099-4300/25/2/306/pdf?version=1675922180Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1099-4300/25/2/306/pdf?version=1675922180Direct OA link when available
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Autoregressive fractionally integrated moving average, Sample entropy, Autoregressive model, Outlier, Computer science, Mathematics, Econometrics, Artificial intelligence, Pattern recognition (psychology), Long memory, Volatility (finance)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
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2024: 1, 2023: 1Per-year citation counts (last 5 years)
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68Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| publication_date | 2023-02-07 |
| publication_year | 2023 |
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