This paper investigates the forecasting ability of four different GARCH models and the Kalman filter method. The four GARCH models applied are the bivariate GARCH, BEKK GARCH, GARCH-GJR and the GARCH-X model. The paper also compares the forecasting ability of the non-GARCH model the Kalman method. Forecast errors based on twenty UK company weekly stock return (based on timevary beta) forecasts ...

This research analyzes how individuals make forecasts based on time series data, and tests an intervention designed to improve forecasting performance. Using data from a controlled laboratory experiment, we find that forecasting behavior systematically deviates from normative predictions: Forecasters over-react to errors in relatively stable environments, but under-react to errors in relatively...

In this paper, we study managers’ errors in decision making for inventory replenishment and how these errors affect their inventory system. In particular, primarily for its expected relationship with the bullwhip effect, we focus on the error of the over-reaction to demand changes and a common contributor of decision making biases: forecasting of demand. By over-reaction we mean that the manage...

The experts considered in this paper are neural networks whose forecasts are combined by another neural network, a gate. For regression problems such an architecture was shown to partly remedy the two main problems in forecasting real world time series: nonstationarity and overfitting. The goal of this paper is to compare the forecasting ability of gated experts (GE) with a that of a single neu...

Traffic flow is widely recognized as an important parameter for road traffic state forecasting. Fuzzy state transform and Kalman filter (KF) have been applied in this field separately. But the studies show that the former method has good performance on the trend forecasting of traffic state variation but always involves several numerical errors. The latter model is good at numerical forecasting...

Wind forecasting is critical in the wind power industry, yet forecasting errors often exist. In order to effectively correct the forecasting error, this study develops a weather adapted bias correction scheme on the basis of an average bias-correction method, which considers the deviation of estimated biases associated with the difference in weather type within each unit of the statistical samp...

forecasting of municipal waste generation is a critical challenge for decision making and planning,because proper planning and operation of a solid waste management system is intensively affected by municipal solid waste (msw) streams analysis and accurate predictions of solid waste quantities generated. due to dynamic and complexity of solid waste management system, models by artificial intell...

Available methods for testing the statistical significance of an improvement in postsample forecasting from one model over another are briefly reviewed. These methods are based on a preselected partitioning of the data into a sample period (to be used for model specification/estimation) and a postsample forecasting period, to be used only for model comparison/evaluation. Given that one expects ...

The impact of assimilating Argo data into an initial field on the short-term forecasting accuracy of temperature and salinity is quantitatively estimated by using a forecasting system of the western North Pacific, on the base of the Princeton ocean model with a generalized coordinate system (POMgcs). This system uses a sequential multigrid three-dimensional variational (3DVAR) analysis scheme t...