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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 on the relationships and differences between statistics and data science. Some data scientists argue that statistics is not necessary for data science as statistics delivers simple explanations and data science delivers results. Therefore, this article aims to stimulate debate and discourse among both academics and practitioners in these fields. The findings reveal the need for stakeholders to accept the inherent advantages and disadvantages within the science of statistics and data science. The science of statistics enables data science (aiding its reliability and validity), and data science expands the application of statistics to Big Data. Data scientists should accept the contribution and importance of statistics and statisticians must humbly acknowledge the novel capabilities made possible through data science and support this field of study with their theoretical and pragmatic expertise. Indeed, the emergence of data science does pose a threat to statisticians, but the opportunities for synergies are far greater.
Hossein Hassani; Christina Beneki; Emmanuel Sirimal Silva; Nicolas Vandeput; Dag Øivind Madsen. The science of statistics versus data science: What is the future? Technological Forecasting and Social Change 2021, 173, 121111 .
AMA StyleHossein Hassani, Christina Beneki, Emmanuel Sirimal Silva, Nicolas Vandeput, Dag Øivind Madsen. The science of statistics versus data science: What is the future? Technological Forecasting and Social Change. 2021; 173 ():121111.
Chicago/Turabian StyleHossein Hassani; Christina Beneki; Emmanuel Sirimal Silva; Nicolas Vandeput; Dag Øivind Madsen. 2021. "The science of statistics versus data science: What is the future?" Technological Forecasting and Social Change 173, no. : 121111.
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.
Hossein Hassani; Xu Huang; Emmanuel Silva. The Human Digitalisation Journey: Technology First at the Expense of Humans? Information 2021, 12, 267 .
AMA StyleHossein Hassani, Xu Huang, Emmanuel Silva. The Human Digitalisation Journey: Technology First at the Expense of Humans? Information. 2021; 12 (7):267.
Chicago/Turabian StyleHossein Hassani; Xu Huang; Emmanuel Silva. 2021. "The Human Digitalisation Journey: Technology First at the Expense of Humans?" Information 12, no. 7: 267.
The ongoing COVID-19 pandemic is disrupting the fashion industry and forcing fashion businesses to accelerate their digital transformation. The increased need for more sustainable fashion business operations, when coupled with the prospect that business might never be as usual again, calls for innovative e-commerce led practices. Recently, stakeholders have been experimenting with the idea of introducing digital humans for a more active role in fashion through the developments in artificial intelligence, virtual, augmented and mixed reality. As there is a lack of all-important empirical evidence on the consumer's propensity to interact with digital humans, we aim to quantitatively analyse consumer attitudes towards the propensity to interact with digital humans to uncover insights to help fashion businesses seeking to diversify their operations. The results reveal interesting, and statistically significant insights which can be useful for fashion business stakeholders for designing, developing, testing, and marketing digital human-based solutions. Besides, our findings contribute current insights to the existing literature on how consumers interact with digital humans, where research tends to be scarce.
Emmanuel Sirimal Silva; Francesca Bonetti. Digital humans in fashion: Will consumers interact? Journal of Retailing and Consumer Services 2021, 60, 102430 .
AMA StyleEmmanuel Sirimal Silva, Francesca Bonetti. Digital humans in fashion: Will consumers interact? Journal of Retailing and Consumer Services. 2021; 60 ():102430.
Chicago/Turabian StyleEmmanuel Sirimal Silva; Francesca Bonetti. 2021. "Digital humans in fashion: Will consumers interact?" Journal of Retailing and Consumer Services 60, no. : 102430.
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.
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 StyleHossein 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 StyleHossein 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.
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.
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 StyleHossein 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 StyleHossein 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.
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.
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 StyleHossein 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 StyleHossein 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.
This paper investigates consumer's attitudes towards fashion product assortment in UK mid-market department stores. It aims to determine whether changes to assortment will increase purchase intention and help regain competitive advantage through aligning customer perceptions of product quality and fit with brand image. Our findings challenge the traditional role of the department store in curating fashion assortment. We find that increases in perceived quality, perceptions of brand portfolio and brand fit will increase the purchase intention of UK mid-market department store consumers, whilst reduced assortment sizes would lead to a decrease in purchase intent.
Shannon Donnelly; Liz Gee; Emmanuel Sirimal Silva. UK mid-market department stores: Is fashion product assortment one key to regaining competitive advantage? Journal of Retailing and Consumer Services 2020, 54, 102043 .
AMA StyleShannon Donnelly, Liz Gee, Emmanuel Sirimal Silva. UK mid-market department stores: Is fashion product assortment one key to regaining competitive advantage? Journal of Retailing and Consumer Services. 2020; 54 ():102043.
Chicago/Turabian StyleShannon Donnelly; Liz Gee; Emmanuel Sirimal Silva. 2020. "UK mid-market department stores: Is fashion product assortment one key to regaining competitive advantage?" Journal of Retailing and Consumer Services 54, no. : 102043.
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 literature on inequality, we study quarterly UK data covering the period from 1975Q1 to 2016Q1. We find that the boosted forecasting models, at forecasting horizons of up to one year, have to differing extents predictive value for changes in the six different inequality measures. Evidence of predictability is stronger on balance when we use information criteria that result in relatively parsimonious forecasting models than information criteria that are more generous in this regard. In addition to lagged inequality measures, stock-market developments and fiscal deficits, and to a lesser extent the real interest rate, economic policy uncertainty, and output growth turn out to be predictors that are often selected by the algorithm.
Christian Pierdzioch; Rangan Gupta; Hossein Hassani; Emmanuel Sirimal Silva. Forecasting changes of economic inequality: A boosting approach. The Social Science Journal 2019, 1 -17.
AMA StyleChristian Pierdzioch, Rangan Gupta, Hossein Hassani, Emmanuel Sirimal Silva. Forecasting changes of economic inequality: A boosting approach. The Social Science Journal. 2019; ():1-17.
Chicago/Turabian StyleChristian Pierdzioch; Rangan Gupta; Hossein Hassani; Emmanuel Sirimal Silva. 2019. "Forecasting changes of economic inequality: A boosting approach." The Social Science Journal , no. : 1-17.
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 luxury fashion house—as an example, we compare several parametric and nonparametric forecasting techniques to determine the best univariate forecasting model for “Burberry” Google Trends. In addition, we also introduce singular spectrum analysis as a useful tool for denoising fashion consumer Google Trends and apply a recently developed hybrid neural network model to generate forecasts. Our initial results indicate that there is no single univariate model (out of ARIMA, exponential smoothing, TBATS, and neural network autoregression) that can provide the best forecast of fashion consumer Google Trends for Burberry across all horizons. In fact, we find neural network autoregression (NNAR) to be the worst contender. We then seek to improve the accuracy of NNAR forecasts for fashion consumer Google Trends via the introduction of singular spectrum analysis for noise reduction in fashion data. The hybrid neural network model (Denoised NNAR) succeeds in outperforming all competing models across all horizons, with a majority of statistically significant outcomes at providing the best forecast for Burberry’s highly seasonal fashion consumer Google Trends. In an era of big data, we show the usefulness of Google Trends, denoising and forecasting consumer behaviour for the fashion industry.
Emmanuel Sirimal Silva; Hossein Hassani; Dag Øivind Madsen; Liz Gee. Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends. Social Sciences 2019, 8, 111 .
AMA StyleEmmanuel Sirimal Silva, Hossein Hassani, Dag Øivind Madsen, Liz Gee. Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends. Social Sciences. 2019; 8 (4):111.
Chicago/Turabian StyleEmmanuel Sirimal Silva; Hossein Hassani; Dag Øivind Madsen; Liz Gee. 2019. "Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends." Social Sciences 8, no. 4: 111.
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 Regression (MSSA-QR model). The proposed risk measure (PLVaR) and forecasting model (MSSA-QR) is used to forecast the worst cases of waterborne disease outbreaks in 22 European and North American countries based on socio-economic and environmental indicators. The results show that the proposed method perfectly forecasts the worst case scenario for less common waterborne diseases whilst the forecasting of more common diseases requires more socio-economic and environmental indicators.
Hossein Hassani; Mohammad Reza Yeganegi; Emmanuel Sirimal Silva; Fatemeh Ghodsi. Risk management, signal processing and econometrics: A new tool for forecasting the risk of disease outbreaks. Journal of Theoretical Biology 2019, 467, 57 -62.
AMA StyleHossein Hassani, Mohammad Reza Yeganegi, Emmanuel Sirimal Silva, Fatemeh Ghodsi. Risk management, signal processing and econometrics: A new tool for forecasting the risk of disease outbreaks. Journal of Theoretical Biology. 2019; 467 ():57-62.
Chicago/Turabian StyleHossein Hassani; Mohammad Reza Yeganegi; Emmanuel Sirimal Silva; Fatemeh Ghodsi. 2019. "Risk management, signal processing and econometrics: A new tool for forecasting the risk of disease outbreaks." Journal of Theoretical Biology 467, no. : 57-62.
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 validated and reported high accuracy rates for predicting the risk of freshwater fish disease emergence in England. Our findings suggest that the disease monitoring strategy employed in England could be successful at preventing disease emergence in certain parts of England, as areas in which there were high fish introductions were not correlated with high disease emergence (which was to be expected from the literature). We further tested our model’s predictions with actual disease emergence data using Chi-Square tests and test of Mutual Information. The results identified areas that require further attention and resource allocation to curb future freshwater disease emergence successfully.
Hossein Hassani; Emmanuel S. Silva; Marine Combe; Demetra Andreou; Mansi Ghodsi; Mohammad Reza Yeganegi; Rodolphe E. Gozlan. A Support Vector Machine Based Approach for Predicting the Risk of Freshwater Disease Emergence in England. Stats 2019, 2, 89 -103.
AMA StyleHossein Hassani, Emmanuel S. Silva, Marine Combe, Demetra Andreou, Mansi Ghodsi, Mohammad Reza Yeganegi, Rodolphe E. Gozlan. A Support Vector Machine Based Approach for Predicting the Risk of Freshwater Disease Emergence in England. Stats. 2019; 2 (1):89-103.
Chicago/Turabian StyleHossein Hassani; Emmanuel S. Silva; Marine Combe; Demetra Andreou; Mansi Ghodsi; Mohammad Reza Yeganegi; Rodolphe E. Gozlan. 2019. "A Support Vector Machine Based Approach for Predicting the Risk of Freshwater Disease Emergence in England." Stats 2, no. 1: 89-103.
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 has been at the forefront of the big climate data analytics implementations and exhaustive research have been carried out covering a variety of topics. This paper aims to present an outlook of big data in climate change studies over the recent years by investigating and summarising the current status of big data applications in climate change related studies. It is also expected to serve as a one-stop reference directory for researchers and stakeholders with an overview of this trending subject at a glance, which can be useful in guiding future research and improvements in the exploitation of big climate data.
Hossein Hassani; Xu Huang; Emmanuel Silva. Big Data and Climate Change. Big Data and Cognitive Computing 2019, 3, 12 .
AMA StyleHossein Hassani, Xu Huang, Emmanuel Silva. Big Data and Climate Change. Big Data and Cognitive Computing. 2019; 3 (1):12.
Chicago/Turabian StyleHossein Hassani; Xu Huang; Emmanuel Silva. 2019. "Big Data and Climate Change." Big Data and Cognitive Computing 3, no. 1: 12.
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 model which can consider data with different series lengths as forecasts are predictions into the future. Following applications which consider published forecasts generated via unknown time series models and forecasts from univariate models, we achieve promising results whereby the proposed multivariate approach succeeds in extracting the auxiliary information in a given forecast for generating a new and more accurate forecast, along with statistically significant accuracy gains in certain cases. In addition, the impact of filtering and the use of Google Trends within the proposed methodology is also considered. Overall, we find conclusive evidence which suggests a sound opportunity to exploit the forecastability of auxiliary information contained within existing forecasts.
Emmanuel Sirimal Silva; Hossein Hassani; Mansi Ghodsi; Zara Ghodsi. Forecasting with auxiliary information in forecasts using multivariate singular spectrum analysis. Information Sciences 2018, 479, 214 -230.
AMA StyleEmmanuel Sirimal Silva, Hossein Hassani, Mansi Ghodsi, Zara Ghodsi. Forecasting with auxiliary information in forecasts using multivariate singular spectrum analysis. Information Sciences. 2018; 479 ():214-230.
Chicago/Turabian StyleEmmanuel Sirimal Silva; Hossein Hassani; Mansi Ghodsi; Zara Ghodsi. 2018. "Forecasting with auxiliary information in forecasts using multivariate singular spectrum analysis." Information Sciences 479, no. : 214-230.
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 NNAR models on both raw and denoised (with Singular Spectrum Analysis) tourism demand series, generate forecasts and compare the results. Thereafter, the denoised NNAR forecasts are also compared with parametric and nonparametric benchmark forecasting models. Contrary to the deseasonalising hypothesis, we find statistically significant evidence which supports the denoising hypothesis for improving the accuracy of NNAR forecasts. Thus, it is noise and not seasonality which hinders NNAR forecasting capabilities.
Emmanuel Sirimal Silva; Hossein Hassani; Saeed Heravi; Xu Huang. Forecasting tourism demand with denoised neural networks. Annals of Tourism Research 2018, 74, 134 -154.
AMA StyleEmmanuel Sirimal Silva, Hossein Hassani, Saeed Heravi, Xu Huang. Forecasting tourism demand with denoised neural networks. Annals of Tourism Research. 2018; 74 ():134-154.
Chicago/Turabian StyleEmmanuel Sirimal Silva; Hossein Hassani; Saeed Heravi; Xu Huang. 2018. "Forecasting tourism demand with denoised neural networks." Annals of Tourism Research 74, no. : 134-154.
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 into a benchmark dataset for epileptic study with five categories, consisting of 100 EEG recordings per category. The results from the SSA based approach are xcompared with those from discrete wavelet transform before proposing a hybrid SSA and principal component analysis based approach for improving accuracy levels further.
Hossein Hassani; Mohammad Reza Yeganegi; Emmanuel Sirimal Silva. A New Signal Processing Approach for Discrimination of EEG Recordings. Stats 2018, 1, 155 -168.
AMA StyleHossein Hassani, Mohammad Reza Yeganegi, Emmanuel Sirimal Silva. A New Signal Processing Approach for Discrimination of EEG Recordings. Stats. 2018; 1 (1):155-168.
Chicago/Turabian StyleHossein Hassani; Mohammad Reza Yeganegi; Emmanuel Sirimal Silva. 2018. "A New Signal Processing Approach for Discrimination of EEG Recordings." Stats 1, no. 1: 155-168.
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 exploited in this paper with causality tests which include the 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.
Xu Huang; Emmanuel Silva; Hossein Hassani. Causality between Oil Prices and Tourist Arrivals. Stats 2018, 1, 134 -154.
AMA StyleXu Huang, Emmanuel Silva, Hossein Hassani. Causality between Oil Prices and Tourist Arrivals. Stats. 2018; 1 (1):134-154.
Chicago/Turabian StyleXu Huang; Emmanuel Silva; Hossein Hassani. 2018. "Causality between Oil Prices and Tourist Arrivals." Stats 1, no. 1: 134-154.
Cryptocurrency has been a trending topic over the past decade, pooling tremendous technological 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 features due to its unique architecture, which also determined its worldwide efficiency, applicability and data intensive characteristics. This paper introduces and summarises the interactions between two significant concepts in the digitalized world, i.e., cryptocurrency and Big Data. Both subjects are at the forefront of technological research, and this paper focuses on their convergence and comprehensively reviews the very recent applications and developments after 2016. Accordingly, we aim to present a systematic review of the interactions between Big Data and cryptocurrency and serve as the one stop reference directory for researchers with regard to identifying research gaps and directing future explorations.
Hossein Hassani; Xu Huang; Emmanuel Silva. Big-Crypto: Big Data, Blockchain and Cryptocurrency. Big Data and Cognitive Computing 2018, 2, 34 .
AMA StyleHossein Hassani, Xu Huang, Emmanuel Silva. Big-Crypto: Big Data, Blockchain and Cryptocurrency. Big Data and Cognitive Computing. 2018; 2 (4):34.
Chicago/Turabian StyleHossein Hassani; Xu Huang; Emmanuel Silva. 2018. "Big-Crypto: Big Data, Blockchain and Cryptocurrency." Big Data and Cognitive Computing 2, no. 4: 34.
The inflation rate is a key economic indicator for which forecasters are constantly seeking to improve the accuracy of predictions, so as to enable better macroeconomic decision making. Presented in this paper is a novel approach which seeks to exploit auxiliary information contained within inflation forecasts for developing a new and improved forecast for inflation by modelling with Multivariate Singular Spectrum Analysis (MSSA). Unlike other forecast combination techniques, the key feature of the proposed approach is its use of forecasts, i.e. data into the future, within the modelling process and extracting auxiliary information for generating a new and improved forecast. We consider real data on consumer price inflation in UK, obtained via the Office for National Statistics. A variety of parametric and nonparametric models are then used to generate univariate forecasts of inflation. Thereafter, the best univariate forecast is considered as auxiliary information within the MSSA model alongside historical data for UK consumer price inflation, and a new multivariate forecast is generated. We find compelling evidence which shows the benefits of the proposed approach at generating more accurate medium to long term inflation forecasts for UK in relation to the competing models. Finally, through the discussion, we also consider Google Trends forecasts for inflation within the proposed framework.
Hossein Hassani; Emmanuel Sirimal Silva. Forecasting UK consumer price inflation using inflation forecasts. Research in Economics 2018, 72, 367 -378.
AMA StyleHossein Hassani, Emmanuel Sirimal Silva. Forecasting UK consumer price inflation using inflation forecasts. Research in Economics. 2018; 72 (3):367-378.
Chicago/Turabian StyleHossein Hassani; Emmanuel Sirimal Silva. 2018. "Forecasting UK consumer price inflation using inflation forecasts." Research in Economics 72, no. 3: 367-378.
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.
Hossein Hassani; Xu Huang; Emmanuel Silva. Digitalisation and Big Data Mining in Banking. Big Data and Cognitive Computing 2018, 2, 18 .
AMA StyleHossein Hassani, Xu Huang, Emmanuel Silva. Digitalisation and Big Data Mining in Banking. Big Data and Cognitive Computing. 2018; 2 (3):18.
Chicago/Turabian StyleHossein Hassani; Xu Huang; Emmanuel Silva. 2018. "Digitalisation and Big Data Mining in Banking." Big Data and Cognitive Computing 2, no. 3: 18.
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 show that the Horizontal Multivariate Singular Spectral Analysis (HMSSA) techniques (both Recurrent (-R) and Vector (-V)) consistently outperform the other competing models. More importantly, from the performance of the HMSSA-V model we find conclusive evidence that CO2 can forecast GT, and also predict its direction of change. Our results highlight the superiority of the nonparametric approach of SSA, which in turn, allows us to handle any statistical process: linear or nonlinear, stationary or non-stationary, Gaussian or non-Gaussian.
Hossein Hassani; Emmanuel Sirimal Silva; Rangan Gupta; Sonali Das. Predicting global temperature anomaly: A definitive investigation using an ensemble of twelve competing forecasting models. Physica A: Statistical Mechanics and its Applications 2018, 509, 121 -139.
AMA StyleHossein Hassani, Emmanuel Sirimal Silva, Rangan Gupta, Sonali Das. Predicting global temperature anomaly: A definitive investigation using an ensemble of twelve competing forecasting models. Physica A: Statistical Mechanics and its Applications. 2018; 509 ():121-139.
Chicago/Turabian StyleHossein Hassani; Emmanuel Sirimal Silva; Rangan Gupta; Sonali Das. 2018. "Predicting global temperature anomaly: A definitive investigation using an ensemble of twelve competing forecasting models." Physica A: Statistical Mechanics and its Applications 509, no. : 121-139.