modeling of the connections per day with time series did not give good results (the errors in some case exceeded the 200%), one of the reason of this bad performance was represented by the sharp fluctuations of the number of connections. On the other hand the results obtained when modeling the average number of connections per week were encouraging. In particular, Figure 3 and Figure 4 report r...

An "on-line" time series prediction system EFuNN-T based on a model of evolving fuzzy neural network------EFuNN is presented in this paper. EFuNN, as a particular type of evolving fuzzy neural network, evolves both its structure and parameters to accommodate new coming data. EFuNN-T, as an application of EFuNN in the field of time series prediction, performs "one-step" ahead prediction in an "o...

In the exponential smoothing approach to forecasting, restrictions are often imposed on the smoothing parameters which ensure that certain components are exponentially weighted averages. In this paper, a new general restriction is derived on the basis that the one-step ahead prediction error can be decomposed into permanent and transient components. It is found that this general restriction red...

This study proposes a combination interval prediction based hybrid ensemble (CIPE) model for short-term wind speed prediction. The (CIP) employs the extreme learning machine (ELM) as predictor with biased convex cost function. To relieve heavy burden of hyper-parameter selection function, technique is developed by combining bagging and stacking methods. Multiple CIP models random hyper-paramete...

Wind power is becoming a main alternative energy source to meet the growing electricity needs. Forecasting wind speed important mitigate generation uncertainty and optimize asset utilization. This paper proposes hybrid prediction model with multivariate input multi-step output capability. The synthesizes linear time series regression nonlinear machine learning algorithm. neurons of are determin...

This paper presents an inverse model using chaotic behaviour. The chaos time series inverse model, which uses coupling methods between an inverse model and chaos theory can reconstruct a deterministic and low-dimensional phase space by transforming irregular behaviours of nonlinear time-varying systems into a strange attractor (e.g., a Rossler attractor or a Lorenz attractor), and it can then p...

Cloud providers should ensure QoS while maximizing resources utilization. One optimal strategy is to timely allocate resources in a fine-grained mode according to application's actual resources demand. The necessary precondition of this strategy is obtaining future load information in advance. We propose a multi-step-ahead load forecasting method, KSwSVR, based on statistical learning theory wh...

A simulated annealing (SA) based variable weighted forecast model is proposed to combine and weigh local chaotic model, artificial neural network (ANN), and partial least square support vector machine (PLS-SVM) to build a more accurate forecast model. The hybrid model was built and multistep ahead prediction ability was tested based on daily MSW generation data from Seattle, Washington, the Uni...