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Introduction
Dr. Hossein Hassani has been included in a recently published list by Stanford University and Elsevier of the top 1% of world scientists across various academic disciplines. Currently, Dr. Hassani serves as an Adjunct Professor at Webster University in Vienna, Austria.
Publications
Publications (207)
This research aims to understand the complexities of energy deployment requiring nexus governance solutions. Nexus governance involves coordinating decision-making across policy areas and sectors, seeking compromise among stakeholders with varying positions. The challenge lies in coordinating diverse sectors and stakeholders amidst potentially conf...
We employed a non-parametric causality test based on Singular Spectrum Analysis (SSA) and used the Vector Error Correction Model (VECM) and Information Share Model (IS) to measure the relationship between the futures and spot prices for seven major agricultural commodities in China from 2009 to 2017. We found that the agricultural futures market ha...
In the digital era, social media platforms have become the focal point for public discourse, with a significant impact on shaping societal narratives. However, they are also rife with mis- and disinformation, which can rapidly disseminate and influence public opinion. This paper investigates the propagation of mis- and disinformation on X, a social...
The R package 'GISINTEGRATION' offers a robust solution for efficiently preparing GIS data for advanced
spatial analyses. This package excels in simplifying intrica procedures like data cleaning, normalization,
and format conversion, ensuring that the data are optimally primed for precise and thorough analysis.
This paper presents a comprehensive analysis of the scholarly footprint of ChatGPT, an AI language model, using bibliometric and scientometric methods. The study zooms in on the early outbreak phase from when ChatGPT was launched in November 2022 to early June 2023. It aims to understand the evolution of research output, citation patterns, collabor...
Social trend mining, situated at the confluence of data science and social research, provides a novel lens through which to examine societal dynamics and emerging trends. This paper explores the intricate landscape of social trend mining, with a specific emphasis on discerning leading and lagging trends. Within this context, our study employs socia...
This research underscores the profound implications of Social Intelligence Mining, notably employing open access data and Google Search engine data for trend discernment. Utilizing advanced analytical methodologies, including wavelet coherence analysis and phase difference, hidden relationships and patterns within social data were revealed. These t...
Data preprocessing has been meticulously executed to cover a wide range of datasets, ensuring that variable names are standardized using synonyms. This approach facilitates seamless data integration and analysis across various datasets. While users have the flexibility to modify variable names, the system intelligently ensures that changes are only...
In this paper, we take a city’s budget, which represents the resources that need to be allocated, and test how many blockchain users need to join a voting process of how the city’s resources should be allocated in order to best represent their preferences. This voting process can be tracked very well through the utilization of IoT and smart technol...
This paper presents a comprehensive analysis of the scholarly footprint of ChatGPT, an AI language model, using bibliometric and scientometric methods. The study aims to understand the evolution of research output, citation patterns, collaborative networks, application domains, and future research directions related to ChatGPT. By analyzing data fr...
Long-memory models are frequently used in finance and other fields to capture long-range dependence in time series data. However, correctly identifying whether a process has long memory is crucial. This paper highlights a significant limitation in using the sample autocorrelation function (ACF) to identify long-memory processes. While the ACF estab...
In this paper, the role of the El Niño‐Southern Oscillation (ENSO), measured by the Equatorial Southern Oscillation Index (EQSOI), is used to formally forecast the inflation and GDP growth rates of the United States, advanced (excluding the United States) and emerging countries, as well as the world economy (barring the United States). We rely on u...
Multitudinous health data are continually being produced as our activities, including medicine, evolve into the digital age where data plays a decisive role. Challenges come along as well, concerning the collection, secure storage, verification and secure access to the continuously growing data at such a broad scale before valuable information can...
Digitalisation has enjoyed rapid acceleration during the COVID-19 pandemic on top of the
already fast-paced expansion impacting almost every aspect of daily life. Digital twin technology, which is considered a building block of Metaverse and an important pillar of Industrial revolution 4.0, has also received growing interest. Apart from its signifi...
Over the last few decades, our digitally expanding world has experienced another significant digitalization boost because of the COVID-19 pandemic. Digital transformations are changing every aspect of this world. New technological innovations are springing up continuously, attracting increasing attention and investments. Digital twin, one of the hi...
The United Kingdom (UK) is a world-renowned fashion hub where the economic importance of the tourism sector was recording continuous growth prior to the pandemic. Interestingly, tourism shopping is widely experienced yet seldom discussed from a tourism demand forecasting context. Driven by the potential relevance of tourism shopping and hoping to m...
Purpose. In several research studies, stock price prediction has been explored, and sentiment analysis has been identified as essential for predicting stock price behavior. The availability of news and social media networks and the rapid development of natural language processing methods attracted many researchers to this field. However, there is a...
We introduce two forecasting methods based on a general class of non-linear models called ‘State-Dependent Models’ (SDMs) for tourism demand forecasting. Using a Monte Carlo simulation which generated data from linear and non-linear models, we evidence how estimations from SDMs can capture the level shifts pattern and nonlinearity in data. Next, we...
Singular spectrum analysis (SSA) is a nonparametric method for separating time series data into a sum of small numbers of interpretable components (signal + noise). One of the steps of the SSA method, which is referenced to Embedding, is extremely sensitive to contamination of outliers which are often founded in time series analysis. To reduce the...
Nowadays, with the rapid growth of information spread, investors involve news and sentiments in their financial decision more than before. This paper investigates the effect of technical and fundamental analysis in the form of technical indicators and sentiments of news on Iranian stocks. Several packages and technologies are developed for English...
This article investigates the role of Big Data in situations of psychological stress such as during the recent pandemic caused by the COVID-19 health crisis. Quarantine measures, which are necessary to mitigate pandemic risk, are causing severe stress symptoms to the human body including mental health. We highlight the most common impact factors an...
Introduction Among different forecasting methods, the singular spectrum analysis (SSA) is a powerful nonparametric technique with both filtering and forecasting capabilities. The SSA method breaks down the observational series into two components, i.e. noise and signal, using the eigenvalues and eigenvectors of the trajectory matrix. Then it calcul...
The importance of energy security for the successful functioning of private companies, national economies, and the overall society cannot be underestimated. Energy is a critical infrastructure for any modern society, and its reliable functioning is essential for all economic sectors and for the well-being of everybody. Uncertainty in terms of the a...
Singular spectrum analysis (SSA) is a popular filtering and forecasting method that is
used in a wide range of fields such as time series analysis and signal processing. A commonly used
approach to identify the meaningful components of a time series in the grouping step of SSA is
the utilization of the visual information of eigentriples. Another su...
We conducted a singular and sectoral vulnerability assessment of ESG factors of Dow-30-listed companies by applying the entropy weight method and analyzing each ESG factor’s information contribution to the overall ESG disclosure score. By reducing information entropy information, weaknesses in the structure of a socio-technological system can be id...
The topic of affective computing has been growing rapidly in recent times. In the last five years, the volume of publications in this field has tripled. The question arises which research trends are most in demand today. This can only be judged by analysing the publications that present the results of research. Since researchers have access to the...
Digitization is the emerging process in the current transformation of industry. Understanding the role and socio-economic consequences of digitalization is crucial for the way technology is being deployed in each sector. One of the affected sectors is dentistry. This study highlights the current advances and challenges in integrating and merging ar...
The importance and relevance of the discipline of statistics with the merits of the evolving field of data science continues to be debated in academia and industry. Following a narrative literature review with over 100 scholarly and practitioner-oriented publications from statistics and data science, this article generates a pragmatic perspective o...
The sample ACF is the most common basic tool in analyzing time-series data. This paper provides a theoretical proof that, under some regularity conditions, sample ACF of a given stationary time series is not absolutely summable. Furthermore, it shows that under some mild conditions, the number of positive and negative sample ACFs and their absolute...
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 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 technologic...
The importance of energy security for successful functioning of private companies, national economies, and the overall society should not be underestimated. Energy is a critical infrastructure for any modern society, and its reliable functioning is essential for all economic sectors and for the well-being of everybody. Uncertainty in terms of the a...
Data transparency is fundamental in the monitoring of markets and provides the bases of data‐driven decision‐making among market players. Energy markets and principally the oil market are by nature depending on accurate, timely and high‐quality data, particularly during periods of downturn, or structural change. High‐quality timely energy data play...
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 gain...
This article tackles the complexity of assessing the vulnerability of digitized socio-technological systems by application of Entropy weight method. We present the application of a powerful and universal mathematical tool to data-driven applied systems analysis by bridging the socio-technological gap between organizational structures and system dev...
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 a...
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 aut...
Big Data continues to disrupt the fashion retail industry and has revolutionized traditional business models. Today, both leading fashion brands and new start-ups are using Big Data analytics to improve business operations and maximize profitability. The current paper aims to take stock of the literature on Big Data in fashion and concisely summari...
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...
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 i...
Quarterly Journal of Economic Growth and Development Research(Iran)
Articles in Press, Accepted Manuscript, Available Online from 07 July 2020
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The amount of non-performing loans is one the indicators for assesssing banks credit risk and its high values is a sign of unhealthy of banking...
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...
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 macro level, 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 t...
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. Th...
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 ar...
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 liter...
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 developme...
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...
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. Meanwhi...
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 Cryptocurrenc...
In all fields of quantitative research, analyzing 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 conclus...
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 tes...
This paper examines the predictive power of time-varying risk aversion over payoffs to the carry trade strategy via the cross-quantilogram methodology of Han et al., (2016). Our analysis yields significant evidence of directional predictability from risk aversion to daily carry trade returns tracked by Deutsche Bank G10 Currency Future Harvest Tota...
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...
Singular spectrum analysis (SSA) is a non-parametric forecasting and filtering method that has many applications in a variety of fields such as signal processing, economics and time series analysis. One of the four steps of the SSA, which is called the grouping step, plays a pivotal role in the SSA because reconstruction and forecasting of results...
The focus of this paper is to bring to light the vital issue of energy poverty alleviation and how big data could improve the data collection quality and mechanism. It also explains the vicious circle of low productivity, health risk, environmental pollution and energy poverty and presents currently used energy poverty measures and alleviation poli...
This study examines the very short, short, medium and long-term forecasting ability of different univariate GARCH models of United Kingdom (UK)'s interest rate volatility, using a long span monthly data from May 1836 to June 2018. The main results show the relevance of considering alternative error distributions to the normal distribution when esti...
We use a boosting algorithm to forecast changes in three income- and three consumption-based inequality measures. Unlike the existing literature, which basically deals with in-sample predictability, we analyze the role of large number of predictors in out-of-sample prediction of inequality growth. Further, deviating from the annual data-based liter...
As technology continues to revolutionise today’s economy, Big Data, Blockchain and
Cryptocurrency are rapidly transforming themselves into mainstream functions within the
financial services industry. This book examines each concept individually, analysing the
opportunities and challenges they bring, and exploring the potential for future developmen...
Singular Spectrum Analysis (SSA) is an increasingly popular time series filtering and forecasting technique. Owing to its widespread applications in a variety of fields, there is a growing interest towards improving its forecasting capabilities. As such, this paper takes into consideration the Recurrent forecasting approach in SSA (SSA-R) and prese...
Selection of optimal dimension of trajectory matrix in singular spectrum analysis plays an important role in signal reconstruction from noisy time series. A noisy time series is embedded into a Hankel matrix and the dimension of this matrix depends on the window length considered for a time series. The window length requirement of a time series dep...
Purpose
Big Data is disrupting the fashion retail industry and revolutionising the traditional fashion business models. Nowadays, leading fashion brands and new start-ups are actively engaging with Big Data analytics to enhance their operations and maximise on profitability. In hope of motivating and providing direction to fashion retail managers,...
The literature on mixed-frequency models is relatively recent and has found applications
across economics and finance. The standard application in economics considers the use
of (usually) monthly variables (e.g. industrial production) for predicting/fitting quarterly
variables (e.g. real GDP). This paper proposes a multivariate singular spectrum an...
The Ljung–Box test is one of the most important tests for time series diagnostics and model selection. The Hassani's −[Formula presented] Theorem, however, indicates that the sum of sample autocorrelation function is always −[Formula presented] for any stationary time series with arbitrary length. In this paper, Hassani's −[Formula presented] Theor...
This paper aims to discuss the current state of Google Trends as a useful tool for fashion
consumer analytics, show the importance of being able to forecast fashion consumer trends and then presents a univariate forecast evaluation of fashion consumer Google Trends to motivate more academic research in this subject area. Using Burberry—a British lu...
In this paper, we analyze the potential role of growth in inequality for forecasting real housing returns of the United Kingdom. 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 1975–2016. Our results indicate that...
Disease emergence, in the last decades, has had increasingly disproportionate impacts on aquatic freshwater biodiversity. Here, we developed a new model based on Support Vector Machines (SVM) for predicting the risk of freshwater fish disease emergence in England. Following a rigorous training process and simulations, the proposed SVM model was val...
Climate science as a data-intensive subject has overwhelmingly affected by the era of big data and relevant technological revolutions. The big successes of big data analytics in diverse areas over the past decade have also prompted the expectation of big data and its efficacy on the big problem-climate change. As an emerging topic, climate change h...
This paper takes a novel approach for forecasting the risk of disease emergence by combining risk management, signal processing and econometrics to develop a new forecasting approach. We propose quantifying risk using the Value at Risk criterion and then propose a two staged model based on Multivariate Singular Spectrum Analysis and Quantile Regres...
A new non-parametric subspace-based approach is introduced for unit root test in AR(1) process. The proposed approach contains block-bootstrap and spectrum properties of a time series as well as statistical testing methodology. The simulation result validates the proposed test and indicates its superiority compared with other existent procedures.
The automated Neural Network Autoregressive (NNAR) algorithm from the forecast package in R generates sub-optimal forecasts when faced with seasonal tourism demand data. We propose denoising as a means of improving the accuracy of NNAR forecasts via an application into forecasting monthly tourism demand for ten European countries. Initially, we fit...
Classifying brain activities based on electroencephalogram (EEG) signals is one of the important applications of time series discriminant analysis for diagnosing brain disorders. In this paper, we introduce a new method based on the Singular Spectrum Analysis (SSA) technique for classifying brain activity based on EEG signals via an application int...
In this paper we analyse whether (anthropometric) CO2 can forecast global temperature anomaly (GT) over an annual out-of-sample period of 1907–2012, which corresponds to an initial in-sample of 1880–1906. For our purpose, we use 12 parametric and nonparametric univariate (of GT only) and multivariate (including both GT and CO2) models. Our results...
The internet gives us free access to a variety of published forecasts. Motivated by this increasing availability of data, we seek to determine whether there is a possibility of exploiting auxiliary information contained within a given forecast to generate a new and more accurate forecast. The proposed theoretical concept requires a multivariate mod...
This paper 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 the US and nine European countries are expl...
Cryptocurrency has been a trending topic over the past decade, pooling tremendoustechnological power and attracting investments valued over trillions of dollars on a global scale.The cryptocurrency technology and its network have been endowed with many superior featuresdue to its unique architecture, which also determined its worldwide efficiency,...
Blockchain is disrupting the banking industry and contributing to the increased big data in banking. However, there exists a gap in research and development into blockchain-ed big data in banking from an academic perspective, and this gap is expected to have a significant negative impact on the adoption and development of blockchain technology for...
bicoid is a maternally transcribed gene which plays a pivotal role during the early developmental stage of Drosophila melanogaster by acting as an essential input to the segmentation network. Therefore, fundamental insights into gene cross-regulations of segmentation network expect to be unveiled by presenting an accurate mathematical model for bic...
Numbers of studies have proved the significant influence of climate variables on hydrological series. Considering the pivotal role of the hydroelectric power plants play in the electricity production in Brazil this paper considers the natural hydrological inflow data from 15 major basins and 8 climate variables containing 7 El Niño Southern Oscilla...