— We consider the regression problem for financial time series. Typically, financial time series are non-stationary and volatile in nature. Because of its good generalization p...
Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. ...
Frequently, sequences of state transitions are triggered by specific signals. Learning these triggered sequences with recurrent neural networks implies storing them as different at...
The paper presents a method for times series prediction using a local dynamic modeling based on a three step process. In the first step the input data is embedded in a reconstruct...
In this paper, the problem of time series prediction is studied. A Bayesian procedure based on Gaussian process models using a nonstationary covariance function is proposed. Exper...