dorsal/arxiv
View SchemaDetecting periodicity in experimental data using linear modeling techniques
| Authors | Michael Small, Kevin Judd |
|---|---|
| Categories | |
| ArXiv ID | physics/9810021 |
| URL | https://arxiv.org/abs/physics/9810021 |
| DOI | 10.1103/PhysRevE.59.1379 |
Abstract
Fourier spectral estimates and, to a lesser extent, the autocorrelation function are the primary tools to detect periodicities in experimental data in the physical and biological sciences. We propose a new method which is more reliable than traditional techniques, and is able to make clear identification of periodic behavior when traditional techniques do not. This technique is based on an information theoretic reduction of linear (autoregressive) models so that only the essential features of an autoregressive model are retained. These models we call reduced autoregressive models (RARM). The essential features of reduced autoregressive models include any periodicity present in the data. We provide theoretical and numerical evidence from both experimental and artificial data, to demonstrate that this technique will reliably detect periodicities if and only if they are present in the data. There are strong information theoretic arguments to support the statement that RARM detects periodicities if they are present. Surrogate data techniques are used to ensure the converse. Furthermore, our calculations demonstrate that RARM is more robust, more accurate, and more sensitive, than traditional spectral techniques.
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"abstract": "Fourier spectral estimates and, to a lesser extent, the autocorrelation\nfunction are the primary tools to detect periodicities in experimental data in\nthe physical and biological sciences. We propose a new method which is more\nreliable than traditional techniques, and is able to make clear identification\nof periodic behavior when traditional techniques do not. This technique is\nbased on an information theoretic reduction of linear (autoregressive) models\nso that only the essential features of an autoregressive model are retained.\nThese models we call reduced autoregressive models (RARM). The essential\nfeatures of reduced autoregressive models include any periodicity present in\nthe data. We provide theoretical and numerical evidence from both experimental\nand artificial data, to demonstrate that this technique will reliably detect\nperiodicities if and only if they are present in the data. There are strong\ninformation theoretic arguments to support the statement that RARM detects\nperiodicities if they are present. Surrogate data techniques are used to ensure\nthe converse. Furthermore, our calculations demonstrate that RARM is more\nrobust, more accurate, and more sensitive, than traditional spectral\ntechniques.",
"arxiv_id": "physics/9810021",
"authors": [
"Michael Small",
"Kevin Judd"
],
"categories": [
"physics.data-an",
"nlin.CD",
"physics.comp-ph"
],
"doi": "10.1103/PhysRevE.59.1379",
"title": "Detecting periodicity in experimental data using linear modeling techniques",
"url": "https://arxiv.org/abs/physics/9810021"
},
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