-PC technique is proposed. To determine the practical usefulness of the model, several pseudo forecasting exercises on 8 series of …", namely 1984. After 1984, FNN-PC has the same accuracy in forecasting with respect to the benchmark. …

forecasting tools for regional employment growth. Because of relevant differences in data availability between the former East and …

intervention have both caused the forecasting process an uneasy task. The present paper employs the monetary-portfolio balance … (1990M1-2008M8). The out-of-sample forecasting assessment reveals that the ANNs have outperformed the RW, which in … models by all evaluation criteria. In addition, the findings also show that the modified model has superior forecasting …

The increasing interest aroused by more advanced forecasting techniques, together with the requirement for more … evaluate the forecasting performance of neural modelling relative to that of time series methods at a regional level … compare the forecasting performance of linear models to that of nonlinear alternative approaches. Pre-processed official …

Factor Forests (DFF) for macroeconomic forecasting, which synthesize the recent machine learning, dynamic factor model and … proposed in Zeileis, Hothorn and Hornik (2008). DFTs and DFFs are non-linear and state-dependent forecasting models, which … powerful tree-based machine learning ensembles conditional on the state of the business cycle. The out-of-sample forecasting …

In this study, we analyzed the forecasting and nowcasting performance of a generalized regression neural network (GRNN …

financial index price forecasting. …

method has the best forecasting accuracy with respect to time series models, such as seasonal ARIMA and ARCH models. The …

approach,based on the S-estimation method, to construct forecasting models that are less sensitive to data contamination by … accuracy and sign predictability measures. We find that robust models tend to improve the forecasting accuracy of the AR and of …

This paper applies three universal approximators for forecasting. They are the Artificial Neural Networks, the …