dorsal/arxiv
View SchemaScaling in Non-stationary time series I
| Authors | M. Ignaccolo, P. Allegrini, P. Grigolini, P. Hamilton, B. J. West |
|---|---|
| Categories | |
| ArXiv ID | physics/0301057 |
| URL | https://arxiv.org/abs/physics/0301057 |
| DOI | 10.1016/j.physa.2003.12.034 |
Abstract
Most data processing techniques, applied to biomedical and sociological time series, are only valid for random fluctuations that are stationary in time. Unfortunately, these data are often non stationary and the use of techniques of analysis resting on the stationary assumption can produce a wrong information on the scaling, and so on the complexity of the process under study. Herein, we test and compare two techniques for removing the non-stationary influences from computer generated time series, consisting of the superposition of a slow signal and a random fluctuation. The former is based on the method of wavelet decomposition, and the latter is a proposal of this paper, denoted by us as step detrending technique. We focus our attention on two cases, when the slow signal is a periodic function mimicking the influence of seasons, and when it is an aperiodic signal mimicking the influence of a population change (increase or decrease). For the purpose of computational simplicity the random fluctuation is taken to be uncorrelated. However, the detrending techniques here illustrated work also in the case when the random component is correlated. This expectation is fully confirmed by the sociological applications made in the companion paper. We also illustrate a new procedure to assess the existence of a genuine scaling, based on the adoption of diffusion entropy, multiscaling analysis and the direct assessment of scaling. Using artificial sequences, we show that the joint use of all these techniques yield the detection of the real scaling, and that this is independent of the technique used to detrend the original signal.
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"abstract": "Most data processing techniques, applied to biomedical and sociological time\nseries, are only valid for random fluctuations that are stationary in time.\nUnfortunately, these data are often non stationary and the use of techniques of\nanalysis resting on the stationary assumption can produce a wrong information\non the scaling, and so on the complexity of the process under study. Herein, we\ntest and compare two techniques for removing the non-stationary influences from\ncomputer generated time series, consisting of the superposition of a slow\nsignal and a random fluctuation. The former is based on the method of wavelet\ndecomposition, and the latter is a proposal of this paper, denoted by us as\nstep detrending technique. We focus our attention on two cases, when the slow\nsignal is a periodic function mimicking the influence of seasons, and when it\nis an aperiodic signal mimicking the influence of a population change (increase\nor decrease). For the purpose of computational simplicity the random\nfluctuation is taken to be uncorrelated. However, the detrending techniques\nhere illustrated work also in the case when the random component is correlated.\nThis expectation is fully confirmed by the sociological applications made in\nthe companion paper. We also illustrate a new procedure to assess the existence\nof a genuine scaling, based on the adoption of diffusion entropy, multiscaling\nanalysis and the direct assessment of scaling. Using artificial sequences, we\nshow that the joint use of all these techniques yield the detection of the real\nscaling, and that this is independent of the technique used to detrend the\noriginal signal.",
"arxiv_id": "physics/0301057",
"authors": [
"M. Ignaccolo",
"P. Allegrini",
"P. Grigolini",
"P. Hamilton",
"B. J. West"
],
"categories": [
"physics.data-an",
"cond-mat.stat-mech",
"physics.soc-ph"
],
"doi": "10.1016/j.physa.2003.12.034",
"title": "Scaling in Non-stationary time series I",
"url": "https://arxiv.org/abs/physics/0301057"
},
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