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It is important to understand to what extent, and in what perspectives, cyber attacks can be predicted. Despite its evident importance, this problem was not investigated until very recently, when we proposed using the innovative methodology of gray-box prediction. This methodology advocates the use of gray-box models, which accommodate the statistical properties/phenomena exhibited by the data. Specifically,...
Even though the electricity HPFC (Hourly Price Forward Curve) is still surprisingly under-researched the prediction of electricity prices is highly important in order to keep power plants profitable or in order to optimize the electricity purchases based on future customers demand. In this work two methods to model and predict HPFC based on neural networks will be proposed and compared to more common...
Rainfall forecasting is one of the most imperative and demanding operational responsibilities carried out by meteorological services all over the world. The task is complicated since all decisions are to be taken in the visage of uncertainty. In this article, the traditional data pre-processing technique, moving average is coupled with Artificial Neural Network as MA - ANN to improve the prediction...
The widespread adoption of ubiquitous devices does not only facilitate the connection of billions of people, but has also fuelled a culture of sharing rich, high resolution locations through check-ins. Despite the profusion of GPS and WiFi driven location prediction techniques, the sparse and random nature of check-in data generation have ushered diverse problems, which have prompted the prediction...
Spares have many kinds and complex specifications, its prediction is difficult, for the problem, the paper proposes the use of nonlinear characteristics of BP neural networks and self-learning ability, based on historical data of spares consumption trains the network of all spares to determine its network model, and used for the future consumption forecast for next year. Through the predictive value...
Acute hypotension episodes are one of the hemodynamic instabilities with high mortality rate that is frequent among many groups of patients. Prediction of acute hypotension episodes can help clinicians to diagnose the cause of this physiological disorder and select proper treatment based on this diagnosis. In this study new physiological time series are generated based on heart rate, systolic blood...
Support vector machines, which are based on statistical learning theory and structural risk minimization principle, in theory, ensure the maximum generalization ability of the model. So compared with the neural network model established on the Empirical Risk Minimization principle, they are more comprehensive in theory. In this paper, it applies the support vector machine into building the time series...
A novel prediction model based on Gompertz function and customer life cycle (CLC) is presented in this paper. Firstly, the calling behavior between the inner-net (China Unicom) mobile customers and the outer-net (China Mobile) mobile customers are extracted and analyzed, then fitted a CLC curve by Gompertz function. Furthermore, the different period of CLC is identified according to the fitting curve...
In this paper, adaptive-tree-structure-based fuzzy model is applied to predict chaotic time series. The fuzzy partition of input data set is adaptive to the pattern of data distribution to optimize the number of the subsets automatically by binary-tree model. A fuzzy area around every discriminant edge is set up by the membership functions corresponding to every subset of input data. A complex nonlinear...
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