This study presents a novel Adaptive resonance theory-Counterpropagation neural network (ART-CPN) for solving forecasting problems. The network is based on the ART concept and the CPN learning algorithm for constructing the neural network. The vigilance parameter is used to automatically generate the nodes of the cluster layer for the CPN learning process. This process improves the initial weig...

Under the background of the globalization, the competition among the enterprises becomes more and more fierce. And the speed of the product circulation is faster than before. Under this background, if the company forecasts the product demand more accurate, the company can plan the product planning according to the demand. The companies can improve the competitiveness. In this paper, we apply th...

Before and during the 2010 Soccer World Cup, participants made probabilistic forecasts for the outcomes of the tournament. We examine the relationship between depression levels and performance in this real-world forecasting task. Across two different waves of predictions, for both continuous and categorical classifications of depression, and with multiple measures of prediction accuracy, we fin...

This paper reflects a neural network approach together with the methods of fuzzy time series of forecasting sugar production data.On behalf of forecsaters , time series forecasting that have varied variations is an important issue.One of the such process is the agriculture production and its productivity and it is not hold by an stoichastic process because of great non-linear due to great non-l...

Models based on economic theory have serious problems at forecasting exchange rates better than simple univariate driftless random walk models, especially at short horizons. Multivariate time series models suffer from the same problem. In this paper, we propose to forecast exchange rates with a large Bayesian VAR (BVAR), using a panel of 33 exchange rates vis-a-vis the US Dollar. Since exchange...

This paper presents a hybrid wavelet support vector machines (WSVM) model that combines both wavelet technique and the SVM model for crude oil forecasting. Based on the purpose, the main time series was decomposed to some multi-frequently time series by wavelet theory and these time series were imposed as input data to the SVM for forecasting of crude oil series. To assess the effectiveness of ...

Since Keynes (1930) and Hicks (1939) propounded their theory of normal backwardation, the issue of whether hedgers must pay speculators an insurance premium has remained controversial. Recent theoretical developments incorporating the existence of market imperfections have validated the existence of an insurance premium charged to hedgers by speculators. Owing to differences in data sets and ec...

Many of the talks and posters during this year’s conference will discuss how both ensemble forecasting and atmospheric data assimilation can work synergistically together. We detail provide a brief description of the underlying theoretical basis for this research. The unifying idea is that the chaotic nature of the atmosphere can actually be put to use to improve data assimilation. Ensemble for...

This paper advocates a theory of expectation formation that incorporates many of the central motivations of behavioral finance theory while retaining much of the discipline of the rational expectations approach. We provide a framework in which agents, in an asset pricing model, underparameterize their forecasting model in a spirit similar to Hong, Stein, and Yu (2005) and Barberis, Shleifer, an...

103 104 105 Psychologists have avoided using game theory because of its unrealistic assumptions on human cognitive ability, such as perfectly accurate forecasting, and its large reliance on equilibrium analysis to predict behavior in social interactions. Recent developments in behavioral game theory address these limitations by allowing for bounded and heterogeneous thinking, recognizing limita...