improving time series forecastingaccuracy is an important yet often difficult task.both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. in this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...

Dynamic link resizing is an attractive approach for resource management in virtual private networks (VPNs) serving modern real-time and multimedia traffic. In this paper, we assess the use of linear traffic predictors to dynamically resize the bandwidth of VPN links. We present the results of performance comparisons of three predictors: Gaussian, autoregressive moving average (ARMA) and fractio...

With the development of Internet and computer science, computer network is changing people’s lives. Meanwhile, Network traffic prediction model itself becomes more and more complex. It is an important research direction to quickly and accurately detect the intrusions or attacks. The performance efficiency of a network intrusion detection system is dominated by pattern matching algorithm. Howeve...

To investigate the variability in energy output from a network of photo-voltaic cells, solar radiation was recorded at ten sites every ten minutes in the Pentland Hills to the south of Edinburgh. We identify spatio-temporal auto-regressive moving average (STARMA) models as the most appropriate to address this problem. Although previously considered computationally prohibitive to work with, we s...

in recent years, various time series models have been proposed for financial markets forecasting. in each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. many researchers have compared different time series models together in order to determine more efficient ...

Akaike’s criterion is often used to test composite hypotheses; for example to determine the order of a priori unknown Auto-Regressive and/or Moving Average models. Objections are formulated against Akaike’s criterion and some modifications are proposed. The application of the theory leads to a general technique for AR-model order estimation based on testing pairs of composite hypotheses. This t...

This study focuses on predicting and estimating possible stock assets in a favorable real-time scenario for financial markets without the involvement of outside brokers about broadcast-based trading using various performance factors data metrics. Sample from Y-finance sector was assembled API-based series quite accurate precise. Prestigious machine learning algorithmic performances both classif...

Switchgrass is known as one of the best second-generation lignocellulosic biomasses for bioethanol production. Designing efficient switchgrass-based bioethanol supply chain (SBSC) is an essential requirement for commercializing the bioethanol production from switchgrass. This paper presents a mixed integer linear programming (MILP) model to design SBSC in which bioethanol demand is under auto-r...

In this paper, we present the use of different mathematical models to forecast service requests in support centers (SCs). A successful prediction of service request can help in the efficient management of both human and technological resources that are used to solve these eventualities. A nonlinear analysis of the time series indicates the convenience of nonlinear modeling. Neural models based ...