RTransferEntropy — Quantifying information flow between different time series using effective transfer entropy

Behrendt, Simon; Dimpfl, Thomas; Peter, Franziska J.; Zimmermann, David J.

doi:10.1016/j.softx.2019.100265

Cited by 142 publications

(158 citation statements)

References 23 publications

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“…We tested the performance of our method on synthetic datasets, where we explicitly embedded a variable-lag causal relation, as well as on biological datasets in the context of the application of identifying initiators of collective behavior, and on other two real-world casual datasets. We compared our methods, VL-Granger causality (VL-G) and VL-Transfer entropy (VL-TE), with several existing methods: Granger causality with F-test (G) [7], Copula-Granger method (CG) [24], Spectral Independence Criterion method (SIC) [35], and transfer entropy (TE) [10].…”

Section: Methodsmentioning

confidence: 99%

“…. , X DTW (t − 1), then compute the residual r DT W ; 5 r Y X = r DT W ; end 6 if BIC 1 (r Y X ) BIC 0 (r Y ) then 7 Gran erResult = true 8 else 9 Gran erResult = f alse ; end 10 return Gran erResult,r Y , r Y X ;…”

Section: Vl-granger Causality Operationalizationmentioning

confidence: 99%

“…Given time series X , Y , and the maximum possible lag δ max , and whether we want to check for variable or xed lag FixLa , Algorithm 4 reports whether X causes Y by se ing T ransEResult to be true or false, and by reporting on two transfer entropy values: T X − →Y and T Y − →X . First, if FixLa is true, then we compute the transfer entropy (line 1) using RTransferEntropy(X , Y ) [10]. If FixLa is false, then, we reconstructed X DT W using DTW ReconstructionFunction(X , Y ) in Section 7.3 (line 2).…”

Section: Vl-transfer-entropy Causality Operationalizationmentioning

confidence: 99%

“…If the ratio T (X , Y ) ratio > 1 (Section 7.6), then X causes Y and we set T ransEResult = true (line 5), otherwise, T ransEResult = f alse (line 6). [10]; end 4 if T (X , Y ) ratio > 1 then 5 T ransEResult = true else 6 T ransEResult = f alse ; end…”

Section: Vl-transfer-entropy Causality Operationalizationmentioning

confidence: 99%

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Variable-Lag Granger Causality for Time Series Analysis

Amornbunchornvej

Zheleva

Berger-Wolf

2019

2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

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Granger causality is a fundamental technique for causal inference in time series data, commonly used in the social and biological sciences. Typical operationalizations of Granger causality make a strong assumption that every time point of the e ect time series is in uenced by a combination of other time series with a xed time delay. e assumption of xed time delay also exists in Transfer Entropy, which is considered to be a non-linear version of Granger causality. However, the assumption of the xed time delay does not hold in many applications, such as collective behavior, nancial markets, and many natural phenomena. To address this issue, we develop Variable-lag Granger causality and Variable-lag Transfer Entropy, generalizations of both Granger causality and Transfer Entropy that relax the assumption of the xed time delay and allow causes to in uence e ects with arbitrary time delays. In addition, we propose a method for inferring both variable-lag Granger causality and Transfer Entropy relations. We demonstrate our approaches on an application for studying coordinated collective behavior and other real-world casual-inference datasets and show that our proposed approaches perform be er than several existing methods in both simulated and real-world datasets. Our approaches can be applied in any domain of time series analysis. e so ware of this work is available in the R-CRAN package: VLTimeCausality.

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Section: Methodsmentioning

confidence: 99%

Section: Vl-granger Causality Operationalizationmentioning

confidence: 99%

Section: Vl-transfer-entropy Causality Operationalizationmentioning

confidence: 99%

Section: Vl-transfer-entropy Causality Operationalizationmentioning

confidence: 99%

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Variable-Lag Granger Causality for Time Series Analysis

Amornbunchornvej

Zheleva

Berger-Wolf

2019

2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

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“…We use a procedure introduced by Dimpfl and Peter 48 which outperforms random shuffling in small samples by bootstraping the underlying Markov process, destroying the dependence between the variables but retaining the dynamics of the series. For the calculations of Transfer Entropy we rely on their R package 49 , proving that all the calculations from SW to LW are statistically significant with p-values < 0.001, while some years from LW are less significant or even not statistically significant.…”

Section: Sw Lw Lw Swmentioning

confidence: 99%

Analyzing changes in the complexity of climate in the last four decades using MERRA-2 radiation data

Delgado-Bonal

Marshak

Yang

et al. 2020

Sci Rep

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the energy balance of the earth is controlled by the shortwave and longwave radiation emitted to space. Changes in the thermodynamic state of the system over time affect climate and are noticeable when viewing the system as a whole. In this paper, we study the changes in the complexity of climate in the last four decades using data from the Modern-era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). First, we study the complexity of the shortwave and longwave radiation fields independently using Approximate Entropy and Sample Entropy, observing that the rate of complexity change is faster for shortwave radiation. Then, we study the causality of those changes using Transfer Entropy to capture the non-linear dynamics of climate, showing that the changes are mainly driven by the variations in shortwave radiation. the observed behavior of climatic complexity could be explained by the changes in cloud amount, and we research that possibility by investigating its evolution from a complexity perspective using data from the international Satellite cloud climatology Project (ISCCP). Climate is a complex system 1 composed of many different processes operating at all spatiotemporal scales, showing non-linear relations between its variables in long time spans 2 or its acceleration in the last four decades 3. Understanding the changes in these non-linear relations is of the utmost importance for understanding current climate change and its implications for the future 4,5. Great efforts have been made in the last years to characterize those variations due to its importance for future abrupt changes in the structure of climate, being it potentially harmful to biodiversity and society 6,7. The atmosphere of the Earth can be considered as a closed non-linear thermodynamic system which basically only exchanges radiation with its environment 8. The solar radiation flux maintains the atmosphere in a non-equilibrium thermodynamics situation, generating a climate dynamics which has an influence on the temperature of the planet. The energy balance is modulated by the incoming solar radiation and the outgoing shortwave and longwave Earth's radiation. Changes in the energy balance of the atmosphere are the drivers of climate change. Nowadays, it is believed that the Earth absorbs more energy from the Sun than it is emitted to the space, creating a situation of energy imbalance 9,10. Although those differences can be monitored from the ground, the use of satellite sensors to provide measures of the fluxes of energy to and from Earth offers a more precise view of the system 11. The evolution of the energy imbalance has been investigated using satellite data and models, and it seems to be increasing in the last decades 12,13. While those changes have been argued to be due to human activities 14 , the questions of which of the parameters are changing faster and if that is affecting the dynamics of the system are still open. When reaching the Earth, part of the incoming solar radiation is reflected off clouds and the...

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Oil price shocks and financial stress: Who is the influencer?

Maghyereh,

Ziadat

2023

Int. J Fin Econ

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We contribute to the literature by being the first to examine the direction of causality between the different sources of oil price shocks and financial stress in the global financial markets (OFR), US, other advanced economies (OAE), and emerging markets (EM). Specifically, we aim to empirically answer a key question: Do global oil market shocks drive financial stress, or does financial stress spur oil market shocks? Using a two‐stage methodology based on the structural VAR (SVAR) and entropy‐based analysis over the period January 2000–October 2022, the results show that the links between financial stress and oil shocks are contingent on the type of shock. Within this, oil supply shock is mildly connected to financial stress; oil demand shock is vulnerable to innovation from financial stress; and oil‐specific demand shock has a noticeable time‐variable element wherein the shock prevails at the beginning of the sample and financial stress dominates the transmission at the end of the sample.

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RTransferEntropy — Quantifying information flow between different time series using effective transfer entropy

Cited by 142 publications

References 23 publications

Variable-Lag Granger Causality for Time Series Analysis

Variable-Lag Granger Causality for Time Series Analysis

Analyzing changes in the complexity of climate in the last four decades using MERRA-2 radiation data

Oil price shocks and financial stress: Who is the influencer?

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