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Liu, Ximei,Latif, Zahid,Xiong, Daoqi,Saddozai, Sehrish Khan,Wara, Kaif Ul Korea Information Processing Society 2019 Journal of information processing systems Vol.15 No.5
Stock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known to have a direct influence on the stock markets globally. Given that the stock price data often contain both linear and non-linear patterns, no single model can be adequate in modelling and predicting time series data. The autoregressive integrated moving average (ARIMA) model cannot deal with non-linear relationships, however, it provides an accurate and effective way to process autocorrelation and non-stationary data in time series forecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. As a result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing price of the Shanghai composite index and Shenzhen component index.
Adoption of Big Data Technologies for Communication Management in Large Projects
Suhail Memon,Wang Changfeng,Shahid Rasheed,Zulfiqar Hussain Pathan,Sehrish Khan Saddozai,QiuYixin,Liu Yanping 보안공학연구지원센터 2016 International Journal of Future Generation Communi Vol.9 No.10
Big data introduced several novel opportunities for many organizations worldwide. Big data technologies are now available for businesses of all scales and the organizations are adopting them increasingly to capitalize on various business gains. In this era of growing projects globally, big data is not only assisting in information exchange but also bringing convenience for communication management in large scale projects. This study assesses the level of awareness of communication management professionals regarding Big Data technologies and analyzes the contribution of different factors responsible for the rate of adoption of big data technologies for communication management in large projects. The study is based on online surveys and interviews of different organizations and the academia. The findings show that, among others, fear of job loss is a major hindrance while the enhanced information mobility is the major accelerator towards the adoption of big data technologies for communication management. It further establishes that unawareness of the professionals towards big data technologies plays a negative role in their acceptance.
Ximei Liu,Zahid Latif,Daoqi Xiong,Sehrish Khan Saddozai,Kaif Ul Wara 한국정보처리학회 2019 Journal of information processing systems Vol.15 No.5
Stock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known tohave a direct influence on the stock markets globally. Given that the stock price data often contain both linearand non-linear patterns, no single model can be adequate in modelling and predicting time series data. Theautoregressive integrated moving average (ARIMA) model cannot deal with non-linear relationships, however,it provides an accurate and effective way to process autocorrelation and non-stationary data in time seriesforecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. Asa result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing priceof the Shanghai composite index and Shenzhen component index.