Short term traffic forecasting has been a very important consideration in many areas of transportation research for more than 3 decades. Short-term traffic forecasting based on data driven methods is one of the most dynamic and developing research arenas with enormous published literature. In order to improve forecasting model accuracy of wavelet neural network, an adaptive particle swarm optim...

An empirical test of new forecasting methods derived from a theory of intelligence: the prediction of conflict in Latin America', 'Neurons with graded response have collective computational properties like those of two-state neurons',

With the increasingly drastic competition in the market, consumers’ minds are more and more complex, and the demand fluctuation of product is more and more frequent, and the demand forecasting is more and more important for the management decision-making for enterprises. Based on the phase-space reconstruction of the original demand data by the chaos theory, the reconstructed data are trained i...

This paper uses forecast combination methods to forecast output growth in a seven-country quarterly economic data set covering 1959–1999, with up to 73 predictors per country. Although the forecasts based on individual predictors are unstable over time and across countries, and on average perform worse than an autoregressive benchmark, the combination forecasts often improve upon autoregressive...

We address the task of inferring the future actions of people from noisy visual input. We denote this task activity forecasting. To achieve accurate activity forecasting, our approach models the effect of the physical environment on the choice of human actions. This is accomplished by the use of state-of-the-art semantic scene understanding combined with ideas from optimal control theory. Our u...