This paper investigates the performance of two simple wind power prediction models, an autoregressive one with exogenous input (ARX-model) and a neural network based one, none of which employs weather prediction data. The models are applied for predicting wind power production in three different wind parks, for which data are available. The error of the models is investigated for various foreca...

This study develops an improved fuzzy time series models for forecasting short-term series data. The forecasts were obtained by comparing the proposed improved fuzzy time series, Hwang’s fuzzy time series, and heuristic fuzzy time series. The tourism from Taiwan to the United States was used to build the sample sets which were officially published annual data for the period of 1991–2001. The ro...

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...

In this paper, based on grey system theory, the general GM(1,1) forecasting model for the growth of Japanese Larch in Liaoning Province was set up and it has been proof-tested in model precision.Verified by use of the dates of the Japanese Larch with age of 21 and 22, it has been proved that the model was effective in practice. The relative error of GM(11) model for mean DBH was 2.4%and 3.69%, ...

Artificial neural network theory generally minimises a standard statistical error, such as the sum of squared errors, to learn relationships from the presented data. However, applications in business have shown that real forecasting problems require alternative error measures. Errors, identical in magnitude, cause different costs. To reflect this, a set of asymmetric cost functions is proposed ...

Artificial neural network theory generally minimises a standard statistical error, such as the sum of squared errors, to learn relationships fiom the presented data. However, applications in business have shown that real forecasting problems require alternative error measures. Errors, identical in magnitude, cause different costs. To reflect this, a set of asymmetric cost functions is proposed ...

Keywords: Hybrid multi-model forecasting system Prediction Display markets Mean square error (MSE) Mean absolute percentage error (MAPE) Average square root error (ASRE) a b s t r a c t This paper provides a novel hybrid multi-model forecasting system, with a special focus on the changing regional market demand in the display markets. Through an intensive case study of the ups and downs of the ...

BACKGROUNDS/OBJECTIVE Schistosomiasis is still a major public health problem in China, despite the fact that the government has implemented a series of strategies to prevent and control the spread of the parasitic disease. Advanced warning and reliable forecasting can help policymakers to adjust and implement strategies more effectively, which will lead to the control and elimination of schisto...

Forecasting has often played predominant roles in daily life as necessary measures can be taken to bypass the undesired and detrimental future prompted by this fact, the issue of forecasting becomes one of the most important topics of research for themodern scientists and as a result several innovative forecasting techniques have been developed. Amongst various well-known forecasting techniques...