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SUMMARY:Causality detection methods for time series analysis
DTSTART;VALUE=DATE-TIME:20181004T153000Z
DTEND;VALUE=DATE-TIME:20181004T155000Z
DTSTAMP;VALUE=DATE-TIME:20201125T055507Z
UID:indico-contribution-28894@agenda.infn.it
DESCRIPTION:Speakers: Teddy Craciunescu (National Institute for Lasers\, P
lasma and Raadiation Physics\, Bucharest\, Romania)\nCoupling and synchron
ization are common phenomena that occur in nature\, e.g. in biological\, p
hysiological and environmental systems\, as well as in physics and enginee
red systems. Depending on the coupling strength\, the systems may undergo
phase synchronization\, generalized synchronization\, lag synchronization
and complete synchronization [1]. Lag or intermittent lag synchronization\
, where the difference between the output of one system and the time-delay
ed output of a second system are asymptotically bounded\, is the typical c
ase of the fusion plasma instability control by pace-making techniques [2-
3]. The major issue\, in determining the efficiency of the pacing techniqu
es\, resides in the periodic or quasiperiodic nature of the plasma instabi
lities occurrence. After the perturbation induced by the control systems\,
if enough time is allowed to pass\, the instabilities are bound to reoccu
r. Therefore\, it is crucial to determine an appropriate interval over whi
ch the pacing techniques can have a real influence and an effective trigge
ring capability.\nSeveral independent classes of statistical indicators de
veloped to address this issue are presented. The transfer entropy [4] is a
powerful tool for measuring the causation between dynamical events. The a
mount of information exchanged between two systems depends not only the ma
gnitude but also the direction of the cause-effect relation. Recurrence pl
ots (RP) is an advanced technique of nonlinear data analysis\, revealing a
ll the times when the phase space trajectory of the dynamical system visit
s roughly the same area in the phase space [5]. RP refinement\, called joi
nt recurrence plots (JRPs)\, can be used to relate the behavior of one sig
nal with the one of another. Convergent cross mapping (CCM) [6]\, tests fo
r causation by measuring the extent to which the historical record of one
time series Y values can reliably estimate states of another time series
X. CCM searches for the signature of X in Y’s time series by detecting
whether there is a correspondence between the “library” of points\, in
the attractor manifold built from Y\, and points in the X manifold. The t
wo manifolds are constructed from lagged coordinates of the two time-serie
s. The Weighted Cross Visibility Graph (WCVG) method [7] starts from the i
dea of mapping the coupled time series into a complex network [8] and eval
uates its structural complexity by mean of the Shannon entropy of the modi
fied adjacency matrix (constructed by weighting the connections with the m
etric distance between two connected values in the time series). \nReferen
ces:\n[1] S. Boccaletti et al\, Phys. Rep.\, 366 (2002) 1.\n[2] A. M
urari et al\, Nucl. Fusion 56 (2016) 076008.\n[3] A. Murari et al\, Nuc
l. Fusion 57 (2017) 126057. \n[4] T. Schreiber\, Phys. Rev. Lett. 85 (2
000) 461.\n[5] N. Marwan et al\, Phys. Rep. 438 (2007) 237–329\n[6]
G. Sugihara et al\, Science 338 (2012) 496–500.\n[7] A. Murari et a
l\, Entropy 19-10 (2017) 569.\n[8] S. Mehraban et al\, EPL 103 (2015) 5
0011.\n\nhttps://agenda.infn.it/event/15217/contributions/28894/
LOCATION:INFN-LNF\, Italy Bruno Touschek Auditorium
URL:https://agenda.infn.it/event/15217/contributions/28894/
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