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Time series analysis and prediction are major scientific challenges that find their applications in fields as diverse as finance, biology, economics, meteorology, and so on. Obtaining the method with the least prediction error is one of the difficult problems of financial market and investment analysts. State space modelling is an efficient and flexible method for statistical inference of a broad class of time series and other data. The neural network is an important tool for analyzing time series especially when it is nonlinear and nonstationary. Essential tools for the study of Box-Jenkins methodology, neural networks, and extended Kalman filter were put together. We examine the use of the nonlinear autoregressive neural network method as a prediction technique for financial time series and the application of the extended Kalman filter algorithm to improve the accuracy of the model. As application on a real example, we are analyzing the time series of the daily price of steel over a 790-day period for establishing the superiority of this method over other existing methods. The simulation results using MATLAB and R software show that the model is capable of producing a reasonable accuracy.

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Background: In recent years, cloud computing has grown vastly. Cloud computing represents a new model for IT service delivery and it typically provides over-a-network, ondemand, self-service access, which is dynamically scalable and elastic, using pools of often virtualized resources. However, this new paradigm is facing diverse challenges from many fronts. Methods: We conducted a systematic literature review of potential challenges of cloud computing. Documents that described challenges of cloud computing were collected of routinely. We grouped identified challenges in taxonomy for a focused international dialogue on solutions. Results: Twenty-three potential challenges were identified and classified in three categories: policy and organizational, technical and legal. The first three categories are deeply rooted in well-known challenges of cloud computing. Conclusions: The simultaneous effect of multiple interacting challenges ranging from technical to intangible issues has greatly complicated advances in cloud computing adoption. A systematic framework of challenges of cloud computing will be essential to accelerate the use of this technology for working well in fact and in order to face with respect to mitigating IT-related cloud computing risks.

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