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...

Day-ahead forecasts are required by electricity market investors to make informed decisions on the trading floor. Whereas it is relatively easier predict performance of a few large-scale photovoltaic (PV) systems, large number small-scale PV systems with wide geographical spread poses more challenges because they often not metered for real-time monitoring. This paper proposes an artificial neur...

Modern embedded platforms need sophisticated resource managers in order to utilize their heterogeneous computational resources efficiently. Furthermore, such are subject fluctuating workloads that unforeseeable at design time. Predicting the incoming workload could enhance efficiency of management this situation. But is case? And, if so, how substantial improvement? Does multiple-step-ahead pre...

This paper introduces a new structure in neural networks called TD-CMAC, an extension to the conventional Cerebellar Model Arithmetic Computer (CMAC), having reasonable ability in time series prediction. TD-CMAC, the conventional CMAC and a classical neural network model called Multi-Layer Perceptron (MLP) are simulated and evaluated for 1-hour-ahead prediction and 24-hour-ahead prediction of c...

This research investigates the use of Ridge Polynomial Neural Network (RPNN) as non-linear prediction model to forecast the future trends of financial time series. The network was used for the prediction of one step ahead and five steps ahead of two exchange rate signals; the British Pound to Euro and the Japanese Yen to British Pound. In order to deal with a dynamic behavior which exists in ti...

Forecasting of non-linear time series is a relevant problem in control. Furthermore, an estimate of the uncertainty of the prediction is useful for constructing robust controllers. Multiple-step ahead forecasting has recently been addressed using Gaussian processes, but direct implementations are restricted to small data sets. In this paper we consider multiple-step forecasting for sparse Gauss...

The task of forecasting a time series over a long horizon is commonly tackled b y iterating one-step-ahead predictors.Despite the popularity that this approach gained in the prediction communit y, its design is still plagued by a number of important unresolved issues, the most important being the accumulation of prediction errors. We introduce a local method to learn one-step-ahead predictors w...