Big Data and Causality

Hassani, Hossein; Huang, Xu; Ghodsi, Maryam

doi:10.1007/s40745-017-0122-3

Cited by 38 publications

(21 citation statements)

References 141 publications

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“…However, the energy consumption behaviour can be investigated by the big data analytics in micro level. Besides, a bunch of data mining techniques can be used to examine the causal relationship between variables (Hassani et al , ).…”

Section: Resultsmentioning

confidence: 99%

The relationship between oil prices, oil imports and income level in Turkey: evidence from Fourier approximation

2019

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The study aims at examining possible impacts of the changes in oil prices and income on crude oil import demand in Turkey using monthly data between 1996:1 and 2017:9. To this end, the study uses a recently introduced Fourier Shin cointegration test of Tsong et al. (2016) and Fourier Toda–Yamamoto approach of Nazlioglu et al. (2016). The study also constructs error correction model to estimate the short‐run parameters. Our empirical findings detect that oil imports are more sensitive to changes in income relative to changes in oil price in the long‐run. Furthermore, 81.4 per cent of the disequilibrium of the shocks converges back to the equilibrium level within the next month. Also, the causality test results provide an evidence for conservation hypothesis in Turkey. Thus, energy conservative policies do not have adverse effects on real economic activity. These results include policy implications for future prospects.

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Section: Resultsmentioning

confidence: 99%

The relationship between oil prices, oil imports and income level in Turkey: evidence from Fourier approximation

2019

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“…The era of big data came along with both big opportunities and challenges, almost all science subjects are experiencing overflowing information at unpredictable volume and speeds [1]. As such, revealing the hidden information in big data via Data Mining (DM) techniques has became an emerging trend and ultimate objective for a wide range of studies [2][3][4][5]. As a data intensive subject, banking has been a popular implementation field for researchers with DM skills over the past decades of the information science revolution.…”

Section: Introductionmentioning

confidence: 99%

Digitalisation and Big Data Mining in Banking

Hassani

Huang

Silva

2018

BDCC

Self Cite

116

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Banking as a data intensive subject has been progressing continuously under the promoting influences of the era of big data. Exploring the advanced big data analytic tools like Data Mining (DM) techniques is key for the banking sector, which aims to reveal valuable information from the overwhelming volume of data and achieve better strategic management and customer satisfaction. In order to provide sound direction for the future research and development, a comprehensive and most up to date review of the current research status of DM in banking will be extremely beneficial.Since existing reviews only cover the applications until 2013, this paper aims to fill this research gap and presents the significant progressions and most recent DM implementations in banking post 2013. By collecting and analyzing the trends of research focus, data resources, technological aids, and data analytical tools, this paper contributes to bringing valuable insights with regard to the future developments of both DM and the banking sector along with a comprehensive one stop reference table. Moreover, we identify the key obstacles and present a summary for all interested parties that are facing the challenges of big data.

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“…Further, it is rather difficult to distinguish cyclic and seasonal components in the shorter duration data being used for SHM applications. In view of this, a majority of the research reported on SSA terms the cyclic and seasonal components together as oscillatory components . The eigenvectors obtained from SSA with a window size of 20 are plotted and shown in Figure .…”

Section: Numerical Studiesmentioning

confidence: 99%

Singular spectrum analysis combined with ARMAX model for structural damage detection

Lakshmi

Rao

Gopalakrishnan

2016

Struct. Control Health Monit.

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Time series analysis is being used popularly in structural health monitoring mainly because of its output-only and non-modal approach. Generally, the damage features are extracted either from the coefficients or the prediction errors of the time series models. However, when the incipient damage is small like minor cracks, the damage features of popularly used time series models, constructed using only the coefficients/prediction errors, are not sensitive. Therefore, identifying the presence or exact spatial damage location becomes difficult. In view of this, in this paper, we present an approach to enhance the sensitivity of the damage features by augmenting Singular Spectrum Analysis (SSA) to ARMAX model, enabling it to locate the smaller damage like cracks. The damage index is obtained from the Cepstral distance between any two ARMAX models. Numerical simulation studies have been carried out by considering an example of a simply supported beam girder with single and multiple cracks. Experimental studies on a simply supported RCC beam is conducted to demonstrate the effectiveness of the proposed algorithm. A benchmark problem associated with the bookshelf frame structure, proposed by Engineering Institute -Los Alamos National Laboratory, is used as another example for experimental verification of the proposed technique. SSA is found to improve the sensitivity of the damage features devised from the ARMAX models for detection of minor damage and damage localization on the structures.

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Big Data and Causality

Cited by 38 publications

References 141 publications

The relationship between oil prices, oil imports and income level in Turkey: evidence from Fourier approximation

The relationship between oil prices, oil imports and income level in Turkey: evidence from Fourier approximation

Digitalisation and Big Data Mining in Banking

Singular spectrum analysis combined with ARMAX model for structural damage detection

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