Before 1960, little empirical research was done on forecasting methods. Since then, the literature has grown rapidly, especially in the area of judgmental forecasting. This research supports and adds to the forecasting guidelines proposed before 1960, such as the value of combining forecasts. New findings have led to significant gains in our ability to forecast and to help people to use forecas...

Although many articles have been written concerning the improved accuracy of combined forecasts, sometimes the obvious is overlooked. By combining seasonal indices and cyclical factors with other explanatory variables, forecasting models acquire increased accuracy for out-of-sample predictions.. This paper encourages the use of least squares forecasting models with time series components. It al...

Due to the movement and complexity of the carbon market, traditional monoscale forecasting approaches often fail to capture its nonstationary and nonlinear properties and accurately describe its moving tendencies. In this study, a multiscale ensemble forecasting model integrating empirical mode decomposition (EMD), genetic algorithm (GA) and artificial neural network (ANN) is proposed to foreca...