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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.06112 |
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| _version_ | 1866910441719988224 |
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| author | Blanks, Zachary Brown, Donald E. |
| author_facet | Blanks, Zachary Brown, Donald E. |
| contents | Quantifying the complexity and irregularity of time series data is a primary pursuit across various data-scientific disciplines. Sample entropy (SampEn) is a widely adopted metric for this purpose, but its reliability is sensitive to the choice of its hyperparameters, the embedding dimension $(m)$ and the similarity radius $(r)$, especially for short-duration signals. This paper presents a novel methodology that addresses this challenge. We introduce a Bayesian optimization framework, integrated with a bootstrap-based variance estimator tailored for short signals, to simultaneously and optimally select the values of $m$ and $r$ for reliable SampEn estimation. Through validation on synthetic signal experiments, our approach outperformed existing benchmarks. It achieved a 60 to 90% reduction in relative error for estimating SampEn variance and a 22 to 45% decrease in relative mean squared error for SampEn estimation itself ($p \leq 0.043$). Applying our method to publicly available short-signal benchmarks yielded promising results. Unlike existing competitors, our approach was the only one to successfully identify known entropy differences across all signal sets ($p \leq 0.042$). Additionally, we introduce "EristroPy," an open-source Python package that implements our proposed optimization framework for SampEn hyperparameter selection. This work holds potential for applications where accurate estimation of entropy from short-duration signals is paramount. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_06112 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bayesian Optimization of Sample Entropy Hyperparameters for Short Time Series Blanks, Zachary Brown, Donald E. Applications Quantifying the complexity and irregularity of time series data is a primary pursuit across various data-scientific disciplines. Sample entropy (SampEn) is a widely adopted metric for this purpose, but its reliability is sensitive to the choice of its hyperparameters, the embedding dimension $(m)$ and the similarity radius $(r)$, especially for short-duration signals. This paper presents a novel methodology that addresses this challenge. We introduce a Bayesian optimization framework, integrated with a bootstrap-based variance estimator tailored for short signals, to simultaneously and optimally select the values of $m$ and $r$ for reliable SampEn estimation. Through validation on synthetic signal experiments, our approach outperformed existing benchmarks. It achieved a 60 to 90% reduction in relative error for estimating SampEn variance and a 22 to 45% decrease in relative mean squared error for SampEn estimation itself ($p \leq 0.043$). Applying our method to publicly available short-signal benchmarks yielded promising results. Unlike existing competitors, our approach was the only one to successfully identify known entropy differences across all signal sets ($p \leq 0.042$). Additionally, we introduce "EristroPy," an open-source Python package that implements our proposed optimization framework for SampEn hyperparameter selection. This work holds potential for applications where accurate estimation of entropy from short-duration signals is paramount. |
| title | Bayesian Optimization of Sample Entropy Hyperparameters for Short Time Series |
| topic | Applications |
| url | https://arxiv.org/abs/2405.06112 |