کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5011363 1462591 2018 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Recurrence network measures for hypothesis testing using surrogate data: Application to black hole light curves
ترجمه فارسی عنوان
معیارهای شبکه عود برای آزمون فرضیه ها با استفاده از داده های جایگزین: کاربردی برای منحنی های نور سیاه و سفید
کلمات کلیدی
شبکه های عودت آزمایش فرضیه، تجزیه و تحلیل سری غیر خطی، منحنی های نور سیاه چاله،
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی مکانیک
چکیده انگلیسی

Recurrence networks and the associated statistical measures have become important tools in the analysis of time series data. In this work, we test how effective the recurrence network measures are in analyzing real world data involving two main types of noise, white noise and colored noise. We use two prominent network measures as discriminating statistic for hypothesis testing using surrogate data for a specific null hypothesis that the data is derived from a linear stochastic process. We show that the characteristic path length is especially efficient as a discriminating measure with the conclusions reasonably accurate even with limited number of data points in the time series. We also highlight an additional advantage of the network approach in identifying the dimensionality of the system underlying the time series through a convergence measure derived from the probability distribution of the local clustering coefficients. As examples of real world data, we use the light curves from a prominent black hole system and show that a combined analysis using three primary network measures can provide vital information regarding the nature of temporal variability of light curves from different spectroscopic classes.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Communications in Nonlinear Science and Numerical Simulation - Volume 54, January 2018, Pages 84-99
نویسندگان
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