THE DYNAMICS OF PRICE–VOLUME INFORMATION TRANSFER IN THE CRYPTOCURRENCY MARKETS

2020 ◽  
Vol 23 (05) ◽  
pp. 2050014
Author(s):  
JINGLAN ZHENG ◽  
CHUN-XIAO NIE
Keyword(s):  
Information Flow ◽  
Information Transfer ◽  
Transfer Entropy ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Dynamic Information ◽  
Market Values ◽  
Daily Data ◽  
Flow Of Information

This study examines the information flow between prices and transaction volumes in the cryptocurrency market, where transfer entropy is used for measurement. We selected four cryptocurrencies (Bitcoin, Ethereum, Litecoin and XRP) with large market values, and Bitcoin and BCH (Bitcoin Cash) for hard fork analysis; a hard fork is when a single cryptocurrency splits in two. By examining the real price data, we show that the long-term time series includes too much noise obscuring the local information flow; thus, a dynamic calculation is needed. The long-term and short-term sliding transfer entropy (TE) values and the corresponding [Formula: see text]-values, based on daily data, indicate that there is a dynamic information flow. The dominant direction of which is [Formula: see text]. In addition, the example based on minute Bitcoin data also shows a dynamic flow of information between price and transaction volume. The price–volume dynamics of multiple time scales helps to analyze the price mechanism in the cryptocurrency market.

Forecasting long-term shoreline evolution in highly antrhopized coastal areas

2021 ◽  
Author(s):  
Moisés Álvarez-Cuesta ◽  
Alexandra Toimil ◽  
Iñigo J. Losada
Keyword(s):  
Climate Models ◽  
Regional Scale ◽  
Mediterranean Coast ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Coastal Zones ◽  
Climate Data ◽  
Shoreline Evolution ◽  
Automated Technique

<p>A new numerical model for addressing long-term coastline evolution on a local to regional scale on highly anthropized coasts is presented. The model, named IH-LANS (Long-term ANthropized coastlines Simulation tool), is validated over the period 1990-2020 and applied to obtain an ensemble of end-of-century shoreline evolutions. IH-LANS combines a hybrid (statistical-numerical) deep-water propagation module and a shoreline evolution model. Longshore and cross-shore processes are integrated together with the effects of man-made interventions. For the ease of calibration, an automated technique is implemented to assimilate observations. The model is applied to a highly anthropized 40 km stretch located along the Spanish Mediterranean coast. High space-time resolution climate data and satellite-derived shorelines are used to drive IH-LANS. Observed shoreline evolution (<10 meters of root mean square error, RMSE) is successfully represented while accounting for the effects of nourishments and the construction and removal of groynes, seawalls and breakwaters over time. Then, in order to drive the ensemble of end-of-century shoreline evolutions, wave and water level projections downscaled from different climate models for various emissions scenarios are employed to force the calibrated model. From the forecasted shoreline time-series, information from multiple time-scales is unraveled yielding valuable information for coastal planners. The efficiency and accuracy of the model make IH-LANS a powerful tool for management and climate change adaptation in coastal zones.</p>

scholarly journals Coupling in complex systems as information transfer across time scales

2019 ◽  
Vol 377 (2160) ◽  
pp. 20190094 ◽  
Author(s):  
Milan Paluš
Keyword(s):  
Complex Systems ◽  
Time Scales ◽  
Information Transfer ◽  
Nonlinear Dynamical Systems ◽  
Southern Oscillation ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Computational Statistics ◽  
Dynamical Interaction ◽  
Spatial And Temporal Scales

Complex systems such as the human brain or the Earth's climate consist of many subsystems interacting in intricate, nonlinear ways. Moreover, variability of such systems extends over broad ranges of spatial and temporal scales and dynamical phenomena on different scales also influence each other. In order to explain how to detect cross-scale causal interactions, we review information-theoretic formulation of the Granger causality in combination with computational statistics (surrogate data method) and demonstrate how this method can be used to infer driver-response relations from amplitudes and phases of coupled nonlinear dynamical systems. Considering complex systems evolving on multiple time scales, the reviewed methodology starts with a wavelet decomposition of a multi-scale signal into quasi-oscillatory modes of a limited bandwidth, described using their instantaneous phases and amplitudes. Then statistical associations, in particular, causality relations between phases or between phases and amplitudes on different time scales are tested using the conditional mutual information. As an application, we present the analysis of cross-scale interactions and information transfer in the dynamics of the El Niño Southern Oscillation. This article is part of the theme issue ‘Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences’.

Aging Couples: Benefits and Costs of Long Intimate Relations

2018 ◽  
Author(s):  
Victoria I. Michalowski ◽  
Denis Gerstorf ◽  
Christiane A. Hoppmann
Keyword(s):  
Time Frame ◽  
Multiple Time Scales ◽  
Future Research ◽  
Multiple Time ◽  
Physical And Mental Health ◽  
Short Term ◽  
Intimate Relations ◽  
Benefits And Costs ◽  
Over Time

Aging does not occur in isolation, but often involves significant others such as spouses. Whether such dyadic associations involve gains or losses depends on a myriad of factors, including the time frame under consideration. What is beneficial in the short term may not be so in the long term, and vice versa. Similarly, what is beneficial for one partner may be costly for the other, or the couple unit over time. Daily dynamics between partners involving emotion processes, health behaviors, and collaborative cognition may accumulate over years to affect the longer-term physical and mental health outcomes of either partner or both partners across adulthood and into old age. Future research should move beyond an individual-focused approach to aging and consider the importance of and interactions among multiple time scales to better understand how, when, and why older spouses shape each other’s aging trajectories, both for better and for worse.

Causality and information transfer in systems with extreme events

2020 ◽  
Author(s):  
Milan Palus
Keyword(s):  
Information Theory ◽  
Extreme Events ◽  
Information Transfer ◽  
Complex Dynamics ◽  
Probability Distributions ◽  
Mathematical Formulation ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Theoretic Approach ◽  
Heavy Tailed

<p>The mathematical formulation of causality in measurable terms of predictability was given by the father of cybernetics N. Wiener [1] and formulated for time series by C.W.J. Granger [2]. The Granger causality is based on the evaluation of predictability in bivariate autoregressive models. This concept has been generalized for nonlinear systems using methods rooted in information theory [3,4]. The information-theoretic approach, defining causality as information transfer, has been successful in many applications and generalized to multivariate data and causal networks [e.g., 5]. This approach, rooted in the information theory of Shannon, usually ignores two important properties of complex systems, such as the Earth climate: the systems evolve on multiple time scales and their variables have heavy-tailed probability distributions. While the multiscale character of complex dynamics, such as air temperature variability, can be studied within the Shannonian framework [6, 7], the entropy concepts of Rényi and Tsallis have been proposed to cope with variables with heavy-tailed probability distributions. We will discuss how such non-Shannonian entropy concepts can be applied in inference of causality in systems with heavy-tailed probability distributions and extreme events, using examples from the climate system.</p><p>This study was supported by the Czech Science Foundation, project GA19-16066S.</p><p> </p><p> [1] N. Wiener, in: E. F. Beckenbach (Editor), Modern Mathematics for Engineers (McGraw-Hill, New York, 1956)</p><p>[2] C.W.J. Granger, Econometrica 37 (1969) 424</p><p>[3] K. Hlaváčková-Schindler et al., Phys. Rep. 441 (2007)  1</p><p>[4] M. Paluš, M. Vejmelka, Phys. Rev. E 75 (2007) 056211</p><p>[5] J. Runge et al., Nature Communications 6 (2015) 8502</p><p>[6] M. Paluš, Phys. Rev. Lett. 112 (2014) 078702</p><p> [7] N. Jajcay, J. Hlinka, S. Kravtsov, A. A. Tsonis, M. Paluš, Geophys. Res. Lett. 43(2) (2016) 902–909</p>

DATA DISCRETIZATION FOR THE TRANSFER ENTROPY IN FINANCIAL MARKET

2013 ◽  
Vol 12 (04) ◽  
pp. 1350019 ◽  
Author(s):  
XUEJIAO WANG ◽  
PENGJIAN SHANG ◽  
JINGJING HUANG ◽  
GUOCHEN FENG
Keyword(s):  
Financial Market ◽  
Information Flow ◽  
Coarse Graining ◽  
Transfer Entropy ◽  
Linear Modeling ◽  
Information Theoretic ◽  
Model Free ◽  
Temporal Correlations ◽  
Data Discretization ◽  
Flow Of Information

Recently, an information theoretic inspired concept of transfer entropy has been introduced by Schreiber. It aims to quantify in a nonparametric and explicitly nonsymmetric way the flow of information between two time series. This model-free based on Shannon entropy approach in principle allows us to detect statistical dependencies of all types, i.e., linear and nonlinear temporal correlations. However, we always analyze the transfer entropy based on the data, which is discretized into three partitions by some coarse graining. Naturally, we are interested in investigating the effect of the data discretization of the two series on the transfer entropy. In our paper, we analyze the results based on the data which are generated by the linear modeling and the ARFIMA modeling, as well as the dataset consists of seven indices during the period 1992–2002. The results show that the higher the degree of data discretization get, the larger the value of the transfer entropy will be, besides, the direction of the information flow is unchanged along with the degree of data discretization.

scholarly journals Intracellular calcium stores mediate a synapse-specific form of metaplasticity at hippocampal dendritic spines

2018 ◽  
Author(s):  
Gaurang Mahajan ◽  
Suhita Nadkarni
Keyword(s):  
Dendritic Spines ◽  
Activity Patterns ◽  
Long Term Potentiation ◽  
Multiple Time Scales ◽  
Calcium Stores ◽  
Multiple Time ◽  
Additional Layer ◽  
Term Potentiation ◽  
Activity Dependent

ABSTRACTLong-term plasticity mediated by NMDA receptors supports input-specific, Hebbian forms of learning at excitatory CA3-CA1 connections in the hippocampus. An additional layer of stabilizing mechanisms that act globally as well as locally over multiple time scales may be in place to ensure that plasticity occurs in a constrained manner. Here, we investigate the potential role of calcium (Ca2+) stores associated with the endoplasmic reticulum (ER) in the local regulation of plasticity dynamics at individual CA1 synapses. Our study is spurred by (1) the curious observation that ER is sparsely distributed in dendritic spines, but over-represented in large spines that are likely to have undergone activity-dependent strengthening, and (2) evidence suggesting that ER motility within synapses can be rapid, and accompany activity-regulated spine remodeling. Based on a physiologically realistic computational model for ER-bearing CA1 spines, we characterize the contribution of IP3-sensitive Ca2+ stores to spine Ca2+ dynamics during activity patterns mimicking the induction of long-term potentiation (LTP) and depression (LTD). Our results suggest graded modulation of the NMDA receptor-dependent plasticity profile by ER, which selectively enhances LTD induction. We propose that spine ER can locally tune Ca2+-based plasticity on an as-needed basis, providing a braking mechanism to mitigate runaway strengthening at potentiated synapses. Our model suggests that the presence of ER in the CA1 spine may promote re-use of synapses with saturated strengths.

scholarly journals Medium and long-term wind energy forecasting method considering multi-scale periodic pattern

2020 ◽  
Vol 182 ◽  
pp. 01002
Author(s):  
Yisha Lin ◽  
Zongxiang Lu ◽  
Ying Qiao ◽  
Mingjie Li ◽  
Zhifeng Liang
Keyword(s):  
Wind Power ◽  
Meteorological Condition ◽  
Parametric Estimation ◽  
Cumulative Distribution ◽  
Multiple Time Scales ◽  
Periodic Pattern ◽  
Multiple Time ◽  
Energy Forecasting ◽  
Multi Scale

Medium and long-term weather sequence forecast becomes unreliable beyond two weeks since the weather is a chaotic system. Using values of same months for electricity prediction of wind power is the usual method. This approach defaults wind power output with annual cycle law. However, the periodic pattern can be very complicated in fact with multiple time scales. This paper proposes an approach with multi-scale periodic pattern considered. The application of parametric estimation on cumulative distribution function avoids the difficulty of predicting the power curve. Meteorological condition is considered to some extent via multi-scale periodic pattern explored basing on historical energy data. This work is an exploration for medium and long-term wind power forecasting that can well adapt to existing conditions. It has better prediction accuracy than the method without multi-scale periodicity considered.

scholarly journals Calculating Transfer Entropy from Variance-Covariance Matrices Provides Insight into Allosteric Communication in ERK2

2020 ◽  
Author(s):  
Luisa Garcia Michel ◽  
Clara Keirns ◽  
Benjamin Ahlbrecht ◽  
Daniel Barr
Keyword(s):  
Information Flow ◽  
Information Transfer ◽  
Experimental Studies ◽  
Mapk Signaling ◽  
Coupled Systems ◽  
Transfer Entropy ◽  
Entropy Analysis ◽  
Biomolecular Systems ◽  
Approximate Methods ◽  
Advantages And Disadvantages

<p>Transfer entropy methods provide an approach to understanding asymmetric information flow in coupled systems, with particular application to understanding allosteric interactions in biomolecular systems. Transfer entropy analysis holds the potential to reveal pathways or networks of residues that are coupled in their information flow and thus give new insights into folding and binding dynamics. Most current methods for calculating transfer entropy require very long simulations and almost equally long calculations of joint probability histograms to compute the information transfer that make these methods either functionally intractable or statistically unreliable. Available approximate methods based on graph and network theory approaches are rapid but lose sensitivity to the chemical nature of the biomolecules and thus are not applicable in mutation studies. We show that reliable estimates of the transfer entropy can be obtained from the variance-covariance matrix of atomic fluctuations, which converges quickly and retains sensitivity to the full chemical profile of the biomolecular system. We validate our method on ERK2, a well-studied kinase involved in the MAPK signaling cascade for which considerable computational, experimental, and mutation data are available. We present the results of transfer entropy analysis on data obtained from molecular dynamics simulations of wild type active and inactive ERK2, along with mutants Q103A, I84A, L73P, and G83A. We show that our method is consistent with the results of computational and experimental studies on ERK2, and we provide a method for interpreting networks of interconnected residues in the protein from a perspective of allosteric coupling. We introduce new insights about possible allosteric activity of the extreme N-terminal region of the kinase, which to date has been under-explored in the literature and may provide an important new direction for kinase studies. We also describe evidence that suggests activation may occur by different paths or routes in different mutants. Our results highlight systematic advantages and disadvantages of each method for calculating transfer entropy and show the important role of transfer entropy analysis for understanding allosteric behavior in biomolecular systems.</p>

scholarly journals Variability and Change in Adolescents’ Prosocial Behavior Across Multiple Time Scales

2021 ◽  
Author(s):  
Lysanne te Brinke ◽  
Suzanne van de Groep ◽  
Renske van der Cruijsen ◽  
Eveline Crone
Keyword(s):  
Prosocial Behavior ◽  
Time Scales ◽  
Perspective Taking ◽  
Prosocial Behaviors ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Late Adolescents ◽  
Daily Data ◽  
Affective Processes ◽  
One Year

We examined variability and change in adolescents’ prosocial behaviors directed to peers and friends across four time scales: two-years, one-year, two-monthly, and daily. Data from three longitudinal datasets with a total of 569 adolescents (55.7% girl, Mage = 15.23, SD = 3.90) were included. The overall time-related stability of prosocial behavior across time scales was moderate to excellent. Variability did not differ between early (age 10-15) and late (age 16-21) adolescence. Late adolescents reported higher mean levels and larger two-year increases. Finally, results indicated that prosocial behaviors measured over longer periods (i.e., two-years and one-year) are positively associated with reflective processes (perspective taking), whereas prosocial behaviors measured over shorter periods (i.e., two-monthly) are positively associated with affective processes (empathy).

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