Chaotic time series prediction

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