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Prof. Hossein Hassani
Webster Vienna Private University

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0 Forecasting
0 Prediction
0 Time Series Analysis
0 Singular Spectrum Analysis
0 Signal proceeding

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Forecasting
Singular Spectrum Analysis
Prediction
Time Series Analysis

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Journal article
Published: 29 June 2021 in Information
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The ongoing COVID-19 pandemic has enhanced the impact of digitalisation as a driver of transformation and advancements across almost every aspect of human life. With the majority actively embracing smart technologies and their benefits, the journey of human digitalisation has begun. Will human beings continue to remain solitary unaffected beings in the middle of the whirlpool—a gateway to the completely digitalised future? This journey of human digitalisation probably started much earlier, before we even realised. This paper, in the format of an objective review and discussion, aims to investigate the journey of human digitalisation, explore the reality of domination between technology and humans, provide a better understanding of the human value and human vulnerability in this fast transforming digital era, so as to achieve valuable and insightful suggestion on the future direction of the human digitalisation journey.

ACS Style

Hossein Hassani; Xu Huang; Emmanuel Silva. The Human Digitalisation Journey: Technology First at the Expense of Humans? Information 2021, 12, 267 .

AMA Style

Hossein Hassani, Xu Huang, Emmanuel Silva. The Human Digitalisation Journey: Technology First at the Expense of Humans? Information. 2021; 12 (7):267.

Chicago/Turabian Style

Hossein Hassani; Xu Huang; Emmanuel Silva. 2021. "The Human Digitalisation Journey: Technology First at the Expense of Humans?" Information 12, no. 7: 267.

Journal article
Published: 28 June 2021 in Big Data and Cognitive Computing
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The launch of the United Nations (UN) 17 Sustainable Development Goals (SDGs) in 2015 was a historic event, uniting countries around the world around the shared agenda of sustainable development with a more balanced relationship between human beings and the planet. The SDGs affect or impact almost all aspects of life, as indeed does the technological revolution, empowered by Big Data and their related technologies. It is inevitable that these two significant domains and their integration will play central roles in achieving the 2030 Agenda. This research aims to provide a comprehensive overview of how these domains are currently interacting, by illustrating the impact of Big Data on sustainable development in the context of each of the 17 UN SDGs.

ACS Style

Hossein Hassani; Xu Huang; Steve MacFeely; Mohammad Entezarian. Big Data and the United Nations Sustainable Development Goals (UN SDGs) at a Glance. Big Data and Cognitive Computing 2021, 5, 28 .

AMA Style

Hossein Hassani, Xu Huang, Steve MacFeely, Mohammad Entezarian. Big Data and the United Nations Sustainable Development Goals (UN SDGs) at a Glance. Big Data and Cognitive Computing. 2021; 5 (3):28.

Chicago/Turabian Style

Hossein Hassani; Xu Huang; Steve MacFeely; Mohammad Entezarian. 2021. "Big Data and the United Nations Sustainable Development Goals (UN SDGs) at a Glance." Big Data and Cognitive Computing 5, no. 3: 28.

Journal article
Published: 05 March 2021 in Annals of Financial Economics
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Uncertainty is known to have negative impact on financial markets through its effects on investors’ decisions. In the wake of the “Great Recession”, quite a few recent studies have highlighted the role of uncertainty in predicting in-sample movements of interest rates. Since in-sample predictability does not guarantee out-of-sample forecasting gains, in this paper, we used historical daily and monthly data for the US to forecast mean and volatility of interest rate. The results show that changes in uncertainty measure movements fail to add any significant statistical gains to the forecast of interest rates for the US. In other words, policy makers in the US are not likely to improve their accuracy of future movements of the policy rate’s mean and volatility by incorporating information derived from changes in metrics of uncertainty.

ACS Style

Hossein Hassani; Mohammad Reza Yeganegi; Rangan Gupta. HISTORICAL FORECASTING OF INTEREST RATE MEAN AND VOLATILITY OF THE UNITED STATES: IS THERE A ROLE OF UNCERTAINTY? Annals of Financial Economics 2021, 1 .

AMA Style

Hossein Hassani, Mohammad Reza Yeganegi, Rangan Gupta. HISTORICAL FORECASTING OF INTEREST RATE MEAN AND VOLATILITY OF THE UNITED STATES: IS THERE A ROLE OF UNCERTAINTY? Annals of Financial Economics. 2021; ():1.

Chicago/Turabian Style

Hossein Hassani; Mohammad Reza Yeganegi; Rangan Gupta. 2021. "HISTORICAL FORECASTING OF INTEREST RATE MEAN AND VOLATILITY OF THE UNITED STATES: IS THERE A ROLE OF UNCERTAINTY?" Annals of Financial Economics , no. : 1.

Journal article
Published: 19 January 2021 in Stats
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Fusing nature with computational science has been proved paramount importance and researchers have also shown growing enthusiasm on inventing and developing nature inspired algorithms for solving complex problems across subjects. Inevitably, these advancements have rapidly promoted the development of data science, where nature inspired algorithms are changing the traditional way of data processing. This paper proposes the hybrid approach, namely SSA-GA, which incorporates the optimization merits of genetic algorithm (GA) for the advancements of Singular Spectrum Analysis (SSA). This approach further boosts the performance of SSA forecasting via better and more efficient grouping. Given the performances of SSA-GA on 100 real time series data across various subjects, this newly proposed SSA-GA approach is proved to be computationally efficient and robust with improved forecasting performance.

ACS Style

Hossein Hassani; Mohammad Reza Yeganegi; Xu Huang. Fusing Nature with Computational Science for Optimal Signal Extraction. Stats 2021, 4, 71 -85.

AMA Style

Hossein Hassani, Mohammad Reza Yeganegi, Xu Huang. Fusing Nature with Computational Science for Optimal Signal Extraction. Stats. 2021; 4 (1):71-85.

Chicago/Turabian Style

Hossein Hassani; Mohammad Reza Yeganegi; Xu Huang. 2021. "Fusing Nature with Computational Science for Optimal Signal Extraction." Stats 4, no. 1: 71-85.

Journal article
Published: 19 December 2020 in Big Data and Cognitive Computing
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This article investigates the impact of big data on the actuarial sector. The growing fields of applications of data analytics and data mining raise the ability for insurance companies to conduct more accurate policy pricing by incorporating a broader variety of data due to increased data availability. The analyzed areas of this paper span from automobile insurance policy pricing, mortality and healthcare modeling to estimation of harvest-, climate- and cyber risk as well as assessment of catastrophe risk such as storms, hurricanes, tornadoes, geomagnetic events, earthquakes, floods, and fires. We evaluate the current use of big data in these contexts and how the utilization of data analytics and data mining contribute to the prediction capabilities and accuracy of policy premium pricing of insurance companies. We find a high penetration of insurance policy pricing in almost all actuarial fields except in the modeling and pricing of cyber security risk due to lack of data in this area and prevailing data asymmetries, for which we identify the application of artificial intelligence, in particular machine learning techniques, as a possible solution to improve policy pricing accuracy and results.

ACS Style

Hossein Hassani; Stephan Unger; Christina Beneki. Big Data and Actuarial Science. Big Data and Cognitive Computing 2020, 4, 40 .

AMA Style

Hossein Hassani, Stephan Unger, Christina Beneki. Big Data and Actuarial Science. Big Data and Cognitive Computing. 2020; 4 (4):40.

Chicago/Turabian Style

Hossein Hassani; Stephan Unger; Christina Beneki. 2020. "Big Data and Actuarial Science." Big Data and Cognitive Computing 4, no. 4: 40.

Research article
Published: 27 September 2020 in International Journal of Finance & Economics
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In this paper, we analyze the potential role of growth in inequality for forecasting realized volatility of the stock market of the UK. In our forecasting exercise, we use linear and nonlinear models as well as measures of absolute and relative consumption and income inequalities at quarterly frequency over the period of 1975 to 2016. Our results indicate that, while linear models incorporating the information of growth in inequality does produce lower forecast errors, these models do not necessarily outperform the univariate linear and nonlinear models based on formal statistical forecast comparison tests, especially in short‐ to medium runs. However, at a one‐year‐ahead horizon, absolute measure of consumption inequality results in significant statistical gains for stock market volatility predictions – possibly due to consumption inequality translating into both political and social uncertainty in the long run.

ACS Style

Hossein Hassani; Mohammad Reza Yeganegi; Rangan Gupta; Riza Demirer. Forecasting stock market (realized) volatility in the United Kingdom: Is there a role of inequality? International Journal of Finance & Economics 2020, 1 .

AMA Style

Hossein Hassani, Mohammad Reza Yeganegi, Rangan Gupta, Riza Demirer. Forecasting stock market (realized) volatility in the United Kingdom: Is there a role of inequality? International Journal of Finance & Economics. 2020; ():1.

Chicago/Turabian Style

Hossein Hassani; Mohammad Reza Yeganegi; Rangan Gupta; Riza Demirer. 2020. "Forecasting stock market (realized) volatility in the United Kingdom: Is there a role of inequality?" International Journal of Finance & Economics , no. : 1.

Article
Published: 29 June 2020 in Annals of Data Science
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Data-driven technologies have been changing every aspect of human life and the fast-developing banking sector with its data-rich nature has become the implementation field of these fast-evolving technologies. Deep learning, as one of the emerging technologies in recent years, has also been inevitably adopted for various improvements in banking. To the best of our knowledge, there is no comprehensive literature review, which focuses on specifically deep learning and its implementations in banking. Therefore, this paper investigates the deep learning technology in-depth and summarizes the relevant applications in banking so to contribute to the existing literature. Moreover, by providing a reliable and up-to-date review, it is also aimed to serve as the one-stop repository for banks and researchers who are interested in embracing deep learning, whilst bringing insights for the directions of future research and implementation.

ACS Style

Hossein Hassani; Xu Huang; Emmanuel Silva; Mansi Ghodsi. Deep Learning and Implementations in Banking. Annals of Data Science 2020, 7, 433 -446.

AMA Style

Hossein Hassani, Xu Huang, Emmanuel Silva, Mansi Ghodsi. Deep Learning and Implementations in Banking. Annals of Data Science. 2020; 7 (3):433-446.

Chicago/Turabian Style

Hossein Hassani; Xu Huang; Emmanuel Silva; Mansi Ghodsi. 2020. "Deep Learning and Implementations in Banking." Annals of Data Science 7, no. 3: 433-446.

Journal article
Published: 07 May 2020 in Signals
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Data transformations are an important tool for improving the accuracy of forecasts from time series models. Historically, the impact of transformations have been evaluated on the forecasting performance of different parametric and nonparametric forecasting models. However, researchers have overlooked the evaluation of this factor in relation to the nonparametric forecasting model of Singular Spectrum Analysis (SSA). In this paper, we focus entirely on the impact of data transformations in the form of standardisation and logarithmic transformations on the forecasting performance of SSA when applied to 100 different datasets with different characteristics. Our findings indicate that data transformations have a significant impact on SSA forecasts at particular sampling frequencies.

ACS Style

Hossein Hassani; Mohammad Reza Yeganegi; Atikur Khan; Emmanuel Sirimal Silva. The Effect of Data Transformation on Singular Spectrum Analysis for Forecasting. Signals 2020, 1, 4 -25.

AMA Style

Hossein Hassani, Mohammad Reza Yeganegi, Atikur Khan, Emmanuel Sirimal Silva. The Effect of Data Transformation on Singular Spectrum Analysis for Forecasting. Signals. 2020; 1 (1):4-25.

Chicago/Turabian Style

Hossein Hassani; Mohammad Reza Yeganegi; Atikur Khan; Emmanuel Sirimal Silva. 2020. "The Effect of Data Transformation on Singular Spectrum Analysis for Forecasting." Signals 1, no. 1: 4-25.

Journal article
Published: 12 April 2020 in AI
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Artificial intelligence (AI) is a rapidly growing technological phenomenon that all industries wish to exploit to benefit from efficiency gains and cost reductions. At the macrolevel, AI appears to be capable of replacing humans by undertaking intelligent tasks that were once limited to the human mind. However, another school of thought suggests that instead of being a replacement for the human mind, AI can be used for intelligence augmentation (IA). Accordingly, our research seeks to address these different views, their implications, and potential risks in an age of increased artificial awareness. We show that the ultimate goal of humankind is to achieve IA through the exploitation of AI. Moreover, we articulate the urgent need for ethical frameworks that define how AI should be used to trigger the next level of IA.

ACS Style

Hossein Hassani; Emmanuel Sirimal Silva; Stephane Unger; Maedeh Tajmazinani; Stephen Mac Feely. Artificial Intelligence (AI) or Intelligence Augmentation (IA): What Is the Future? AI 2020, 1, 143 -155.

AMA Style

Hossein Hassani, Emmanuel Sirimal Silva, Stephane Unger, Maedeh Tajmazinani, Stephen Mac Feely. Artificial Intelligence (AI) or Intelligence Augmentation (IA): What Is the Future? AI. 2020; 1 (2):143-155.

Chicago/Turabian Style

Hossein Hassani; Emmanuel Sirimal Silva; Stephane Unger; Maedeh Tajmazinani; Stephen Mac Feely. 2020. "Artificial Intelligence (AI) or Intelligence Augmentation (IA): What Is the Future?" AI 1, no. 2: 143-155.

Journal article
Published: 05 March 2020 in Economies
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This paper examines the predictive power of time-varying risk aversion over payoffs to the carry trade strategy via the cross-quantilogram methodology. Our analysis yields significant evidence of directional predictability from risk aversion to daily carry trade returns tracked by the Deutsche Bank G10 Currency Future Harvest Total Return Index. The predictive power of risk aversion is found to be stronger during periods of moderate to high risk aversion and largely concentrated on extreme fluctuations in carry trade returns. While large crashes in carry trade returns are associated with significant rises in investors’ risk aversion, we also found that booms in carry trade returns can be predicted at high quantiles of risk aversion. The results highlight the predictive role of extreme investor sentiment in currency markets and regime specific patterns in carry trade returns that can be captured via quantile-based predictive models.

ACS Style

Riza Demirer; Rangan Gupta; Hossein Hassani; Xu Huang. Time-Varying Risk Aversion and the Profitability of Carry Trades: Evidence from the Cross-Quantilogram. Economies 2020, 8, 18 .

AMA Style

Riza Demirer, Rangan Gupta, Hossein Hassani, Xu Huang. Time-Varying Risk Aversion and the Profitability of Carry Trades: Evidence from the Cross-Quantilogram. Economies. 2020; 8 (1):18.

Chicago/Turabian Style

Riza Demirer; Rangan Gupta; Hossein Hassani; Xu Huang. 2020. "Time-Varying Risk Aversion and the Profitability of Carry Trades: Evidence from the Cross-Quantilogram." Economies 8, no. 1: 18.

Application note
Published: 06 February 2020 in Journal of Applied Statistics
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This application note investigates the causal relationship between oil price and tourist arrivals to further explain the impact of oil price volatility on tourism-related economic activities. The analysis itself considers the time domain, frequency domain and information theory domain perspectives. Data relating to US and nine European countries are exploited in this paper with causality tests which include time domain, frequency domain, and Convergent Cross Mapping (CCM). The CCM approach is nonparametric and therefore not restricted by assumptions. We contribute to existing research through the successful and introductory application of an advanced method, and via the uncovering of significant causal links from oil prices to tourist arrivals.

ACS Style

Hossein Hassani; Mansi Ghodsi; Xu Huang; Emmanuel Sirimal Silva. Is there a causal relationship between oil prices and tourist arrivals? Journal of Applied Statistics 2020, 48, 191 -202.

AMA Style

Hossein Hassani, Mansi Ghodsi, Xu Huang, Emmanuel Sirimal Silva. Is there a causal relationship between oil prices and tourist arrivals? Journal of Applied Statistics. 2020; 48 (1):191-202.

Chicago/Turabian Style

Hossein Hassani; Mansi Ghodsi; Xu Huang; Emmanuel Sirimal Silva. 2020. "Is there a causal relationship between oil prices and tourist arrivals?" Journal of Applied Statistics 48, no. 1: 191-202.

Journal article
Published: 16 January 2020 in Big Data and Cognitive Computing
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Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

ACS Style

Hossein Hassani; Christina Beneki; Stephan Unger; Maedeh Taj Mazinani; Mohammad Reza Yeganegi. Text Mining in Big Data Analytics. Big Data and Cognitive Computing 2020, 4, 1 .

AMA Style

Hossein Hassani, Christina Beneki, Stephan Unger, Maedeh Taj Mazinani, Mohammad Reza Yeganegi. Text Mining in Big Data Analytics. Big Data and Cognitive Computing. 2020; 4 (1):1.

Chicago/Turabian Style

Hossein Hassani; Christina Beneki; Stephan Unger; Maedeh Taj Mazinani; Mohammad Reza Yeganegi. 2020. "Text Mining in Big Data Analytics." Big Data and Cognitive Computing 4, no. 1: 1.

Chapter
Published: 19 December 2019 in Fusing Big Data, Blockchain and Cryptocurrency
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This chapter will focus on the interactions between Big Data and the most famous and established use case of Blockchain technology—Cryptocurrency, which has also evolved to function far more than what it initially did, as was introduced in Chap. 3, Sects. 3.1 and 3.3. The interactions between Big Data and Cryptocurrency are briefly categorized into three domains: technical advancements, marketplace application developments, and Big Data analytics in the Cryptocurrency market. We aim to reduce the barriers among academics, practitioners, business professionals, and tech talents by summarizing the most up-to-date advancements and implementations so that it is beneficial for various interested parties in identifying appropriate use cases, research gap, and potential research collaborations.

ACS Style

Hossein Hassani; Xu Huang; Emmanuel Sirimal Silva. Big Data and Cryptocurrency. Fusing Big Data, Blockchain and Cryptocurrency 2019, 77 -98.

AMA Style

Hossein Hassani, Xu Huang, Emmanuel Sirimal Silva. Big Data and Cryptocurrency. Fusing Big Data, Blockchain and Cryptocurrency. 2019; ():77-98.

Chicago/Turabian Style

Hossein Hassani; Xu Huang; Emmanuel Sirimal Silva. 2019. "Big Data and Cryptocurrency." Fusing Big Data, Blockchain and Cryptocurrency , no. : 77-98.

Chapter
Published: 19 December 2019 in Fusing Big Data, Blockchain and Cryptocurrency
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The main interest of this chapter is to present the reader with the idea and benefits of fusing Big Data and Blockchain technology. As the focus of this book is the inclusive fusion of Big Data, Blockchain, and Cryptocurrency, it is important to briefly introduce Big Data first before we delve into its interactions with Blockchain and Cryptocurrency. We begin by summarizing the significance and evolution of Big Data, in order to provide a solid foundational understanding of the same, prior to delving into its infrastructure and Data Mining—the means of analysing Big Data. Thereafter, we move the discussion into the revolutionary impact of Blockchain on FinTech before we consider the opportunities made possible via the fusion of Big Data and Blockchain technology.

ACS Style

Hossein Hassani; Xu Huang; Emmanuel Sirimal Silva. Big Data and Blockchain. Fusing Big Data, Blockchain and Cryptocurrency 2019, 7 -48.

AMA Style

Hossein Hassani, Xu Huang, Emmanuel Sirimal Silva. Big Data and Blockchain. Fusing Big Data, Blockchain and Cryptocurrency. 2019; ():7-48.

Chicago/Turabian Style

Hossein Hassani; Xu Huang; Emmanuel Sirimal Silva. 2019. "Big Data and Blockchain." Fusing Big Data, Blockchain and Cryptocurrency , no. : 7-48.

Chapter
Published: 19 December 2019 in Fusing Big Data, Blockchain and Cryptocurrency
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This chapter focuses on the evolution and interactions between Blockchain and Cryptocurrency from theoretical, technological, and practical aspects. Following a concise look at the Cryptocurrency market and its main players, we introduce the reader to the technicalities underlying Blockchain-ed Cryptocurrency, which enabled its prosperous developments over recent years. In particular, we discuss the intense competitions of Transaction Per Second (TPS) in comparison with traditional means of transactions; the upgrade and developments of Cryptocurrencies that reinforced its distinct products; the diverse functions and medium of exchanges Cryptocurrency brought to the market by those springing up Blockchain-ed DApp platforms; and the advancements of consensus mechanism which fundamentally enabled the booming progression of Blockchain-ed Cryptocurrency.

ACS Style

Hossein Hassani; Xu Huang; Emmanuel Sirimal Silva. Blockchain and Cryptocurrency. Fusing Big Data, Blockchain and Cryptocurrency 2019, 49 -76.

AMA Style

Hossein Hassani, Xu Huang, Emmanuel Sirimal Silva. Blockchain and Cryptocurrency. Fusing Big Data, Blockchain and Cryptocurrency. 2019; ():49-76.

Chicago/Turabian Style

Hossein Hassani; Xu Huang; Emmanuel Sirimal Silva. 2019. "Blockchain and Cryptocurrency." Fusing Big Data, Blockchain and Cryptocurrency , no. : 49-76.

Chapter
Published: 19 December 2019 in Fusing Big Data, Blockchain and Cryptocurrency
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The introductory chapter sets the scene by noting the importance of Big Data, Blockchain, and Cryptocurrency in the modern age whilst providing a brief overview of the history underlying each of these three phenomena. This is followed by details around the need for, and the main purpose of this book, which is the fusion of Big Data, Blockchain, and Cryptocurrency. The chapter concludes with a concise summary of what the readers can expect in the chapters ahead.

ACS Style

Hossein Hassani; Xu Huang; Emmanuel Sirimal Silva. Introduction. Fusing Big Data, Blockchain and Cryptocurrency 2019, 1 -6.

AMA Style

Hossein Hassani, Xu Huang, Emmanuel Sirimal Silva. Introduction. Fusing Big Data, Blockchain and Cryptocurrency. 2019; ():1-6.

Chicago/Turabian Style

Hossein Hassani; Xu Huang; Emmanuel Sirimal Silva. 2019. "Introduction." Fusing Big Data, Blockchain and Cryptocurrency , no. : 1-6.

Chapter
Published: 19 December 2019 in Fusing Big Data, Blockchain and Cryptocurrency
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The previous chapters have investigated the advancements and revolutions empowered by the joint forces of every pair of two out of the three significant concepts underlying the focus of this book. The rapid advancements of each concept have not only broadened the horizon of its own use cases but also promoted the developments of the others. Meanwhile, each concept itself is also positively influenced by the others’ revolution and leads to even more advanced improvements. In this chapter, the technological advancements, wider applications, and challenges resulting from the fusion of Big Data, Blockchain, and Cryptocurrency together will be comprehensively discussed. A subsection is also dedicated to FinTech considering its significance of being the pioneering marketplace for emerging implementations that closely integrated technological advancements.

ACS Style

Hossein Hassani; Xu Huang; Emmanuel Sirimal Silva. Fusing Big Data, Blockchain, and Cryptocurrency. Fusing Big Data, Blockchain and Cryptocurrency 2019, 99 -117.

AMA Style

Hossein Hassani, Xu Huang, Emmanuel Sirimal Silva. Fusing Big Data, Blockchain, and Cryptocurrency. Fusing Big Data, Blockchain and Cryptocurrency. 2019; ():99-117.

Chicago/Turabian Style

Hossein Hassani; Xu Huang; Emmanuel Sirimal Silva. 2019. "Fusing Big Data, Blockchain, and Cryptocurrency." Fusing Big Data, Blockchain and Cryptocurrency , no. : 99-117.

Journal article
Published: 16 December 2019 in Stats
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In all fields of quantitative research, analysing data with missing values is an excruciating challenge. It should be no surprise that given the fragmentary nature of fossil records, the presence of missing values in geographical databases is unavoidable. As in such studies ignoring missing values may result in biased estimations or invalid conclusions, adopting a reliable imputation method should be regarded as an essential consideration. In this study, the performance of singular spectrum analysis (SSA) based on L 1 norm was evaluated on the compiled δ 13 C data from East Africa soil carbonates, which is a world targeted historical geology data set. Results were compared with ten traditionally well-known imputation methods showing L 1 -SSA performs well in keeping the variability of the time series and providing estimations which are less affected by extreme values, suggesting the method introduced here deserves further consideration in practice.

ACS Style

Hossein Hassani; Mahdi Kalantari; Zara Ghodsi. Evaluating the Performance of Multiple Imputation Methods for Handling Missing Values in Time Series Data: A Study Focused on East Africa, Soil-Carbonate-Stable Isotope Data. Stats 2019, 2, 457 -467.

AMA Style

Hossein Hassani, Mahdi Kalantari, Zara Ghodsi. Evaluating the Performance of Multiple Imputation Methods for Handling Missing Values in Time Series Data: A Study Focused on East Africa, Soil-Carbonate-Stable Isotope Data. Stats. 2019; 2 (4):457-467.

Chicago/Turabian Style

Hossein Hassani; Mahdi Kalantari; Zara Ghodsi. 2019. "Evaluating the Performance of Multiple Imputation Methods for Handling Missing Values in Time Series Data: A Study Focused on East Africa, Soil-Carbonate-Stable Isotope Data." Stats 2, no. 4: 457-467.

Journal article
Published: 16 December 2019 in Physica A: Statistical Mechanics and its Applications
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Theoretical results and empirical evidences indicate that the Ljung–Box test is sensitive to the number of lags (H) involved in the test. In time series literature, different values are suggested for H. This paper is concerned with the selecting optimal number of lags H in Ljung–Box test such that the actual size of the test does not exceed the test’s level while the power of the test does not fall under specific value. A simulation study is employed to investigate the effect of selecting an improper values of H on the actual size and power of the Ljung–Box test. The results confirm that an optimal value of H depends on the time series’ length as well as the test’s level. The comparison results with currently used approaches in the literature confirm that the commonly used techniques are not suggesting a proper value for H.

ACS Style

Hossein Hassani; Mohammad Reza Yeganegi. Selecting optimal lag order in Ljung–Box test. Physica A: Statistical Mechanics and its Applications 2019, 541, 123700 .

AMA Style

Hossein Hassani, Mohammad Reza Yeganegi. Selecting optimal lag order in Ljung–Box test. Physica A: Statistical Mechanics and its Applications. 2019; 541 ():123700.

Chicago/Turabian Style

Hossein Hassani; Mohammad Reza Yeganegi. 2019. "Selecting optimal lag order in Ljung–Box test." Physica A: Statistical Mechanics and its Applications 541, no. : 123700.

Journal article
Published: 28 November 2019 in Mathematical Biosciences
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An extended version of Birnbaum–Saunders distribution with five parameters is introduced. Theoretical aspects of five-parameter Birnbaum–Saunders distribution and the maximum likelihood estimation of parameters are presented. The reliability and applicability of the proposed distribution is evaluated using both simulation and real-world data namely bicoid gene expression profile. The findings of this research confirm that the newly proposed five-parameter Birnbaum–Saunders distribution can be utilized to describe the distribution of bicoid gene expression profile.

ACS Style

Hossein Hassani; Mahdi Kalantari; Mohammad Reza Entezarian. A new five-parameter Birnbaum–Saunders distribution for modeling bicoid gene expression data. Mathematical Biosciences 2019, 319, 108275 .

AMA Style

Hossein Hassani, Mahdi Kalantari, Mohammad Reza Entezarian. A new five-parameter Birnbaum–Saunders distribution for modeling bicoid gene expression data. Mathematical Biosciences. 2019; 319 ():108275.

Chicago/Turabian Style

Hossein Hassani; Mahdi Kalantari; Mohammad Reza Entezarian. 2019. "A new five-parameter Birnbaum–Saunders distribution for modeling bicoid gene expression data." Mathematical Biosciences 319, no. : 108275.