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The time series prediction of avian influenza epidemics is a complex issue, because avian influenza has latent seasonality which is difficult to identify. Although researchers have applied a neural network (NN) model and the Box-Jenkins model for the seasonal epidemic series research area, the results are limited. In this study, we develop a new prediction seasonal auto-regressive-based support vector regression (SAR-SVR) model which combines the seasonal auto-regressive (SAR) model with a support vector regression (SVR) model to address this prediction problem to overcome existing limitations. Fast Fourier transformation is also merged into this method to identify the latent seasonality inside the time series. The experiments demonstrate that the developed SAR-SVR method out-performs SVR, Box-Jenkins models and two layer feed forward NN model-both in accuracy and stability in the avian influenza epidemic disease time series prediction.

The forecasting of electricity consumption and demand plays a pivotal role in electric power systems planning. This paper proposes the combination of forecasts from two approaches with the aim of improving the forecasting accuracy, in order to make the best use of the installed transmission and generating capacity. In the first approach, the consumption time series is decomposed by wavelet analysis and a Box-Jenkins model is fitted to each wavelet component, following which the individual components forecasts are added to compute the total consumption forecast. The alternative approach, uses the Singular Spectrum Analysis technique to model the consumption time series in order to shrink the noise level. Thereafter, the Box-Jenkins model is used to forecast the filtered time series, producing a second forecast for the consumption series. Eventually, the two forecasts are combined geometrically in order to minimize the mean square error. The proposed methodology is illustrated by a computational experiment with the time series of residential consumption of electricity in Brazil.