Chaotic time series

In this paper, we investigate the use of a deep learning method, Deep Belief Network (DBN), combined with chaos theory to forecast chaotic time series. DBN should be used to forecast chaotic time series. First, the chaotic time series are analyzed by calculating the largest Lyapunov exponent, reconstructing the time series by phase-space reconstruction and determining the best embedding dimension and the best delay time. When the forecasting model is constructed, the deep belief network is used to feature learning and the neural network is used for prediction.

11p trinhthamhodang9 04-12-2020 22 2   Download

The study uses autoregressive fractionally integrated moving average – fractionally integrated generalized autoregressive conditional heteroskedasticity (ARFIMA-FIGARCH) models and chaos effects to determine nonlinearity properties present on currency ETN returns. The results find that the volatilities of currency ETNs have long-memory, non-stationarity and non-invertibility properties. These findings make the research conclude that mean reversion is a possibility and that the efficient market hypothesis of Fama (1970) became ungrounded on these investment instruments.

23p trinhthamhodang2 21-01-2020 27 2   Download

Computational Physics is now a multidisciplinary line of research. A long time ago, it was not like that, computers were used by physicists only in order to solve classical problems resistant to the analytical approach: One is able to model the problem at hands through, say, a set of coupled differential equations, but the analytical solution for these equations is not available. The solace is the numerical solution, and the old branch of Computational Physics consists in providing fast and precise numerical methods to be applied to these cases....

285p banhkem0908 24-11-2012 43 5   Download

CHAOTIC DYNAMICS Gaurav S. Patel Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada Simon Haykin Communications Research Laboratory, McMaster University, Hamilton, Ontario, Canada (haykin@mcmaster.ca) 4.1 INTRODUCTION In this chapter, we consider another application of the extended Kalman filter recurrent multilayer perceptron (EKF-RMLP) scheme: the modeling of a chaotic time series or one that could be potentially chaotic. The generation of a chaotic process is governed by a coupled set of nonlinear differential or difference equations.

40p khinhkha 29-07-2010 88 10   Download

In this chapter, we consider another application of the extended Kalman filter recurrent multilayer perceptron (EKF-RMLP) scheme: the modeling of a chaotic time series or one that could be potentially chaotic. The generation of a chaotic process is governed by a coupled set of nonlinear differential or difference equations.

40p duongph05 07-06-2010 78 14   Download