Dynamic causality analysis using overlapped sliding windows based on the extended convergent cross-mapping

2021 ◽  
Author(s):  
Xinlei Ge ◽  
Aijing Lin
Keyword(s):  
Causality Analysis ◽  
Sliding Windows ◽  
Convergent Cross Mapping

scholarly journals Causality analysis in process control based on denoising and periodicity-removing CCM

2020 ◽  
Vol 1 (1) ◽  
pp. 25-41
Author(s):  
Qiming Chen ◽  
Xinyi Fei ◽  
Lie Xie ◽  
Dongliu Li ◽  
Qibing Wang
Keyword(s):  
Control System ◽  
Process Control ◽  
Singular Spectrum Analysis ◽  
Process Control System ◽  
Fluctuation Analysis ◽  
Causality Analysis ◽  
Industrial Case Study ◽  
Content Type ◽  
Root Cause ◽  
Convergent Cross Mapping

Purpose1. To improve the causality analysis performance, a novel causality detector based on time-delayed convergent cross mapping (TD-CCM) is proposed in this work. 2. Identify the root cause of plant-wide oscillations in process control system.Design/methodology/approachA novel causality analysis framework is proposed based on denoising and periodicity-removing TD-CCM (time-delayed convergent cross mapping). We first point out that noise and periodicity have adverse effects on causality detection. Then, the empirical mode decomposition (EMD) and detrended fluctuation analysis (FDA) are combined to achieve denoising. The periodicities are effectively removed through singular spectrum analysis (SSA). Following, the TD-CCM can accurately capture the causalities and locate the root cause by analyzing the filtered signals.Findings1. A novel causality detector based on denoising and periodicity-removing time-delayed convergent cross mapping (TD-CCM) is proposed. 2. Simulation studies show that the proposed method is able to improve the causality analysis performance. 3. Industrial case study shows the proposed method can be used to analyze the root cause of plant-wide oscillations in process control system.Originality/value1. A novel causality detector based on denoising and periodicity-removing time-delayed convergent cross mapping (TD-CCM) is proposed. 2. The influences of noise and periodicity on causality analysis are investigated. 3. Simulations and industrial case shows that the proposed method can improve the causality analysis performance and can be used to identify the root cause of plant-wide oscillations in process control system.

The Nexus between Financial Development and Human Development: The Case of Developing Countries

2020 ◽  
Vol 8 (2) ◽  
pp. 68
Author(s):  
Bilgehan Tekin
Keyword(s):  
Developing Countries ◽  
Private Sector ◽  
Human Development ◽  
Financial Development ◽  
Cointegration Analysis ◽  
Annual Data ◽  
Causality Analysis ◽  
Development Index ◽  
Development Levels ◽  
The Relationship

The purpose of this study to examine the relationship between financial development and human development in the health and welfare dimensions of developing countries. This study aims to determine whether the financial developments of the countries have an effect on the basic human development of the individuals and whether human development indicators have an impact on financial development. In this study, the relationship between financial development and human development has been tried to be revealed by using data obtained from developing countries. Financial development levels of the countries were measured with the developed financial development index. The index is calculated by using M3 / GDP, private sector loans / GDP and loans to banks from private sector / GDP ratios. The human development index is calculated by considering various health indicators and GNP per capita. The data includes annual data for the period 1970-2016. Pedroni and Kao cointegration analysis and Dumitrescu & Hurlin panel causality analysis were performed in the study. According to the results of the study, the cointegration relationship was determined between the two variables. There is also a two-way causality between the variables.

Fraud and Bank Performance in Nigeria – Var Granger Causality Analysis

e-Finanse ◽  
2020 ◽  
Vol 16 (1) ◽  
pp. 20-26
Author(s):  
Taiwo A. Muritala ◽  
Muftau A. Ijaiya ◽  
Olatanwa H. Afolabi ◽  
Abdulrasheed B. Yinus
Keyword(s):  
Control Systems ◽  
Granger Causality ◽  
Internal Control ◽  
Banking Sector ◽  
Bank Performance ◽  
Causality Analysis ◽  
Vector Autoregressive ◽  
Granger Causality Analysis ◽  
Long Run ◽  
Multivariate Cointegration

AbstractThis paper examines the causality between fraud and bank performance in Nigeria over the period 2000-2016 for quarterly financial data using Johansen’s Multivariate Cointegration Model and Vector Autoregressive (VAR) Granger Causality analysis. The results show a long-run relationship between the variables. Bank performance was found to be linked to Granger fraud variables and vice versa at 10% significant level. This study reveals that there was a direct causal relationship between bank performance and fraud because increase in fraudulent activities in the banking sector leads to reduction in bank performance. Hence, this study recommends that internal control systems of banks should be strengthened so as to detect and prevent fraud. In this way, bank assets would be protected.

Vicious Cycle of Economic Growth, Pm2.5 and COVID-19 Case of G7 Countries

2020 ◽  
Author(s):  
Rıdvan Karacan
Keyword(s):  
Economic Growth ◽  
Air Pollution ◽  
Particulate Matter ◽  
Fossil Fuels ◽  
Fine Particulate Matter ◽  
Panel Cointegration ◽  
Air Pollutant ◽  
Population Exposure ◽  
Causality Analysis ◽  
G7 Countries

<p>Today, production is carried out depending on fossil fuels. Fossil fuels pollute the air as they contain high levels of carbon. Many studies have been carried out on the economic costs of air pollution. However, in the present study, unlike the former ones, economic growth's relationship with the COVID-19 virus in addition to air pollution was examined. The COVID-19 virus, which was initially reported in Wuhan, China in December 2019 and affected the whole world, has caused many cases and deaths. Researchers have been going on studying how the virus is transmitted. Some of these studies suggest that the number of virus-related cases increases in regions with a high level of air pollution. Based on this fact, it is thought that air pollution will increase the number of COVID-19 cases in G7 Countries where industrial production is widespread. Therefore, the negative aspects of economic growth, which currently depends on fossil fuels, is tried to be revealed. The research was carried out for the period between 2000-2019. Panel cointegration test and panel causality analysis were used for the empirical analysis. Particulate matter known as PM2.5[1] was used as an indicator of air pollution. Consequently, a positive long-term relationship has been identified between PM2.5 and economic growth. This relationship also affects the number of COVID-19 cases.</p><p><br></p><p><br></p><p>[1] "Fine particulate matter (PM2.5) is an air pollutant that poses the greatest risk to health globally, affecting more people than any other pollutant (WHO, 2018). Chronic exposure to PM2.5 considerably increases the risk of respiratory and cardiovascular diseases in particular (WHO, 2018). For these reasons, population exposure to (outdoor or ambient) PM2.5 has been identified as an OECD Green Growth headline indicator" (OECD.Stat).</p>

An Econometric Analysis on the Relationship between Globalisation and Inequality in South Africa

2020 ◽  
Vol 11 (1) ◽  
pp. 89-89
Author(s):  
Jacques de Jongh
Keyword(s):  
South Africa ◽  
Well Being ◽  
Causality Analysis ◽  
Global Integration ◽  
Development Indicators ◽  
Long Run ◽  
The World ◽  
Distribution Of Resources ◽  
Short Run ◽  
The Impact

Globalisation has had an unprecedented impact on the development and well-being of societies across the globe. Whilst the process has been lauded for bringing about greater trade specialisation and factor mobility many have also come to raise concerns on its impact in the distribution of resources. For South Africa in particular this has been somewhat of a contentious issue given the country's controversial past and idiosyncratic socio-economic structure. Since 1994 though, considerable progress towards its global integration has been made, however this has largely coincided with the establishment of, arguably, the highest levels of income inequality the world has ever seen. This all has raised several questions as to whether a more financially open and technologically integrated economy has induced greater within-country inequality (WCI). This study therefore has the objective to analyse the impact of the various dimensions of globalisation (economic, social and political) on inequality in South Africa. Secondary annual time series from 1990 to 2018 were used sourced from the World Bank Development indicators database, KOF Swiss Economic Institute and the World Inequality database. By using different measures of inequality (Palma ratios and distribution figures), the study employed two ARDL models to test the long-run relationships with the purpose to ensure the robustness of the results. Likewise, two error correction models (ECM) were used to analyse the short-run dynamics between the variables. As a means of identifying the casual effects between the variables, a Toda-Yamamoto granger causality analysis was utilised. Keywords: ARDL, Inequality, Economic Globalisation; Social Globalisation; South Africa

Anomaly Detection via Mining Numerical Workflow Relations from Logs

2020 ◽  
Author(s):  
Bo Zhang ◽  
Hongyu Zhang ◽  
Pablo Moscato
Keyword(s):  
Distributed Systems ◽  
Anomaly Detection ◽  
Text Messages ◽  
Distributed File System ◽  
Log Data ◽  
Sliding Windows ◽  
Novel Approach ◽  
Hadoop Distributed File System ◽  
Blue Gene ◽  
Online Anomaly Detection

<div>Complex software intensive systems, especially distributed systems, generate logs for troubleshooting. The logs are text messages recording system events, which can help engineers determine the system's runtime status. This paper proposes a novel approach named ADR (stands for Anomaly Detection by workflow Relations) that employs matrix nullspace to mine numerical relations from log data. The mined relations can be used for both offline and online anomaly detection and facilitate fault diagnosis. We have evaluated ADR on log data collected from two distributed systems, HDFS (Hadoop Distributed File System) and BGL (IBM Blue Gene/L supercomputers system). ADR successfully mined 87 and 669 numerical relations from the logs and used them to detect anomalies with high precision and recall. For online anomaly detection, ADR employs PSO (Particle Swarm Optimization) to find the optimal sliding windows' size and achieves fast anomaly detection.</div><div>The experimental results confirm that ADR is effective for both offline and online anomaly detection. </div>

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