Detection of Cause-Effect Relations Based on Information Granulation and Transfer Entropy

Zhang, Xiangxiang; Hu, Wenkai; Yang, Fan

doi:10.3390/e24020212

Cited by 15 publications

(3 citation statements)

References 51 publications

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“…Mindful activities such as meditation, biofeedback [42] and yoga can help reduce chaotic activities in the brain, reduce entropy, and minimize the effects of anxiety and depression. Any focusing activities improves the mind-reality connection and directs efforts toward improving performance and creativity, which in turn suppresses entropy, increases potential, and maximizes cause-effect information [43].…”

Section: Discussionmentioning

confidence: 99%

Halalopathy: Anxiety and Depression from Logic and Energetic Perspectives

Alzeer

2022

AJBSR

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Section: Discussionmentioning

confidence: 99%

Halalopathy: Anxiety and Depression from Logic and Energetic Perspectives

Alzeer

2022

AJBSR

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“…TE is in an asymmetric form, which makes it possible to measure cause–effect relationships. However, the drawbacks of TE originated from computational complexity [ 45 , 46 ], resulting in identification issues [ 47 ]. X i and Y i are considered two discrete random variables.…”

Section: Methodsmentioning

confidence: 99%

Information flow among stocks, bonds, and convertible bonds

Choi

Jeong

et al. 2023

PLoS ONE

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This study examines the information flow between convertible bonds (CBs) and other investment assets, such as stocks and bonds. In particular, we employ transfer entropy (TE) as a proxy for the causal effect between the two assets considering that one of the most widely used methods, Granger causality, requires strict assumptions. When adopting TE, we find that asymmetric information flow arising between assets depends on macroeconomic phases. The stock and bond markets affected the CB market prior to and during the global financial crisis, respectively. In the post-crisis period, we find no meaningful information exchange between CBs and other investment assets concerning their return series. However, we observe a significant cause–effect relationship between CBs and stocks in the rise–fall patterns of their price series. The findings suggest that the appearance of one-directional information flow depends on macroeconomic conditions and the level of data, for example, return series or price fluctuations. Accordingly, investors could exploit this pattern predictability in their portfolio management. In addition, policymakers must closely monitor the information flow among the three markets. When any two markets exchange information in a state of strong market integration, unbalanced regulation between them could lead to market distortions and regulatory arbitrage.

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“…Accordingly, causality inference, association rule mining, and sequential pattern mining have been exploited to find such directions when detecting correlated alarms. Among these approaches, causality inference detects the causal relations from historical data complemented by process knowledge [12]; commonly used methods include Transfer Entropy [13][14][15], Granger causality [16], and qualitative trend analysis [17]. In [14,15], transfer entropies were exploited and modified to detect the causal relations between alarm signals.…”

Section: Introductionmentioning

confidence: 99%

A First-Out Alarm Detection Method via Association Rule Mining and Correlation Analysis

Li,

Cheng

2023

Entropy

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Alarm systems are commonly deployed in complex industries to monitor the operation status of the production process in real time. Actual alarm systems generally have alarm overloading problems. One of the major factors leading to excessive alarms is the presence of many correlated or redundant alarms. Analyzing alarm correlations will not only be beneficial to the detection of and reduction in redundant alarm configurations, but also help to track the propagation of abnormalities among alarm variables. As a special problem in correlated alarm detection, the research on first-out alarm detection is very scarce. A first-out alarm is known as the first alarm that occurs in a series of alarms. Detection of first-out alarms aims at identifying the first alarm occurrence from a large number of alarms, thus ignoring the subsequent correlated alarms to effectively reduce the number of alarms and prevent alarm overloading. Accordingly, this paper proposes a new first-out alarm detection method based on association rule mining and correlation analysis. The contributions lie in the following aspects: (1) An association rule mining approach is presented to extract alarm association rules from historical sequences based on the FP-Growth algorithm and J-Measure; (2) a first-out alarm determination strategy is proposed to determine the first-out alarms and subsequent alarms through correlation analysis in the form of a hypothesis test on conditional probability; and (3) first-out rule screening criteria are proposed to judge whether the rules are redundant or not and then consolidated results of first-out rules are obtained. The effectiveness of the proposed method is tested based on the alarm data generated by a public simulation platform.

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Detection of Cause-Effect Relations Based on Information Granulation and Transfer Entropy

Cited by 15 publications

References 51 publications

Halalopathy: Anxiety and Depression from Logic and Energetic Perspectives

Halalopathy: Anxiety and Depression from Logic and Energetic Perspectives

Information flow among stocks, bonds, and convertible bonds

A First-Out Alarm Detection Method via Association Rule Mining and Correlation Analysis

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