In this paper one-step-ahead and multiple-step-ahead predictions of time series in disturbed open loop and closed loop systems using Gaussian process models and TS-fuzzy models are described. Gaussian process models are based on the Bayesian framework where the conditional distribution of output measurements is used for the prediction of the system outputs. For one-step-ahead prediction a local...

In this paper, we review our work on a time series forecasting methodology based on the combination of unsupervised clustering and artificial neural networks. To address noise and non–stationarity, a common approach is to combine a method for the partitioning of the input space into a number of subspaces with a local approximation scheme for each subspace. Unsupervised clustering algorithms hav...

Extensive empirical experience suggests that traditional forecasting approaches are subject to more or less severe model misspecifications which affect true (out-of-sample) oneas well as multi-step ahead forecasting performances. The main problems are due to non-stationarity and non-Gaussianity. In order to overcome these difficulties, we propose a prototypical design derived from a traditional...

Support Vector Regression (SVR) has been widely applied in time series forecasting. Considering long term predictions, iterative predictions perform many one-step-ahead predictions until the desired horizon is achieved. This process accumulates the error from previous predictions and may affect the quality of forecasts. In order to improve long term iterative predictions a hybrid multiple Autor...

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 shows the "Sequential PrQjection Algorithm" application results, originated by the Bell System [1] and [2], forecasting the yearly representative traffic" values (YRV*) of III different route types of four years data (1983-1986), one step ahead. A seasonal algorithm using monthly data (MRV*) [3], will support the former, however, requiring an exact determination of parameters to succ...

Damped trend exponential smoothing models have gained importance in empirical studies due to their remarkable forecasting performance. This paper derives their theoretical forecast error variance, based on the implied ARIMA model, as algebraic function of the structural parameters. As a consequence, the minimum mean squared error (MMSE) forecasts as well as the h-step ahead theoretical forecast...