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 …

financial index price forecasting. …

-to-estimate and explain, performs best for forecasting. Our conservative out-of-sample forecast evaluation, using data …

financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH … 30, 2005. The experiment shows that, under both varying and fixed forecasting schemes, the SVR-based GARCH outperforms … examined to the free parameters. Keywords: recurrent support vector regression ; GARCH model ; volatility forecasting …

characteristics that are useful to predict anomaly returns (i.e., the forecasting model becomes more dense). Looking at specific …

returns plays an important role for volatility forecasting. Additionally, models utilizing a logarithmic transformation of the … an easy-to-use and accurate tool for realized variance forecasting, whose performance may potentially be further improved …

financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH … 30, 2005. The experiment shows that, under both varying and fixed forecasting schemes, the SVR-based GARCH outperforms … examined to the free parameters. Keywords: recurrent support vector regression ; GARCH model ; volatility forecasting …

This paper analyses inflation forecasting power of artificial neural networks with alternative univariate time series … models for Turkey. The forecasting accuracy of the models is compared in terms of both static and dynamic forecasts for the …, provide better one-step ahead forecasting performance. However, unobserved components model turns out to be the best performer …