کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
757837 1462603 2017 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Visibility graph analysis for re-sampled time series from auto-regressive stochastic processes
ترجمه فارسی عنوان
تجزیه و تحلیل نمودار قابل رویت برای سری های زمانی دوباره نمونه برداری شده از فرایندهای تصادفی خودرگرسیونی
کلمات کلیدی
نمودار قابل رویت ؛ فرآیندهای AR؛ تجزیه و تحلیل سری های زمانی غیرخطی
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی مکانیک
چکیده انگلیسی


• We study the effects of the correlation length of a time series on the exponential degree distributions of visibility graphs.
• We systematically show the effects of re-sampling time delays on constructing visibility graphs.
• We propose the decorrelation time as the maximal delay for the re-sampling algorithm.

Visibility graph (VG) and horizontal visibility graph (HVG) play a crucial role in modern complex network approaches to nonlinear time series analysis. However, depending on the underlying dynamic processes, it remains to characterize the exponents of presumably exponential degree distributions. It has been recently conjectured that there is a critical value of exponent λc=ln3/2,λc=ln3/2, which separates chaotic from correlated stochastic processes. Here, we systematically apply (H)VG analysis to time series from autoregressive (AR) models, which confirms the hypothesis that an increased correlation length results in larger values of λ > λc. On the other hand, we numerically find a regime of negatively correlated process increments where λ < λc, which is in contrast to this hypothesis. Furthermore, by constructing graphs based on re-sampled time series, we find that network measures show non-trivial dependencies on the autocorrelation functions of the processes. We propose to choose the decorrelation time as the maximal re-sampling delay for the algorithm. Our results are detailed for time series from AR(1) and AR(2) processes.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Communications in Nonlinear Science and Numerical Simulation - Volume 42, January 2017, Pages 396–403
نویسندگان
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