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...

Multiplicative trend exponential smoothing has received very little attention in the literature. It involves modelling the local slope by smoothing successive ratios of the local level, and this leads to a forecast function that is the product of level and growth rate. By contrast, the popular Holt method uses an additive trend formulation. It has been argued that more real series have multipli...

-Electricity authorities in the UAE have not been successful in developing reliable and accurate models of system peak load. In this study, we develop a time-series-based decisionsupport system that integrates data management, model base management, simulation, graphic display, and statistical analysis to provide near-optimal forecasting models. The model base includes a variety of time-series ...

The quality of statistical analyses of functional neuroimages is studied after applying various preprocessing methods. We present wavelet-based denoising as an alternative to Gaussian smoothing, the standard denoising method in statistical parametric mapping (SPM). The wavelet-based denoising schemes are extensions of WaveLab routines, using the symmetric orthogonal cubic spline wavelet basis. ...

Reliable automatic methods are needed for statistical online monitoring of noisy time series. Application of a robust scale estimator allows to use adaptive thresholds for the detection of outliers and level shifts. We propose a fast update algorithm for the Qn estimator and show by simulations that it leads to more powerful tests than other highly robust scale estimators.

In this study, a hybrid synergy model integrating exponential smoothing and neural network is proposed for financial time series prediction. The proposed model attempts to incorporate the linear characteristics of an exponential smoothing model and nonlinear patterns of neural network to create a “synergetic” model via the linear programming technique. For verification, two real-world financial...