Is the choice of an error measure to identify the most accurate forecasting method a question of personal taste? It appears that this may be the case although the papers by Armstrong and Collopy and by Fildes argue that it should not be. Carbone and Armstrong (1982) found that Root Mean Square Error (RMSE) was the most preferred measure of forecast accuracy. This is despite the fact that it is ...

Article history: Received 13 February 2016 Accepted 4 July 2016 Available online 5 August 2016 To compare the accuracy of different forecasting approaches an error measure is required. Many error measures have been proposed in the literature, however in practice there are some situations where different measures yield different decisions on forecasting approach selection and there is no agreeme...

We develop and validate a medium-term solar irradiance forecasting model by adopting predicted meteorological variables from the US National Weather Service’s (NWS) forecasting database as inputs to an Artificial Neural Network (ANN) model. Since the inputs involved are the same as the ones available from a recently validated forecasting model, we include mean bias error (MBE), root mean square...

Grey theory [1] has been proposed over 30 years. Great endeavor has been devoted to increase the forecasting precision. One of methods treated the forecasting error to become the modified grey forecasting model. Hsu and Wen [2] modified original GM (1,1) models are improved by using residual modifications with Markov chain sign estimations. Hsu and Chen [3] improved grey GM (1,1) model, using a...

Due to the high risk associated with international transactions, exchange rate forecasting is a challenging and important field in modern time series analysis. The difficulty in forecasting arises from the nonlinearity and non-stationarity inherent in exchange rate dynamics. To address these problems, this study proposes a hybrid model that couples two effective feature extraction techniques, p...