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In this paper, we employed both traditional and chaotic approaches for time series forecasting. It concerns the forecasting of cash withdrawal amounts at automated teller machines (ATMs) for which the NN5 forecasting competitions data was used. The data consists of 111 time series representing daily withdrawal amounts. In the first method (traditional, non-chaotic) missing values of the time series...
An approach based on chaos theory and fuzzy neural network (FNN) is proposed for chaotic time series prediction. Firstly, C-C algorithm is applied to estimate the delay time of chaotic signal. Grassberger-Procaccia (G-P) algorithm and least squares regression are employed to calculate the correlation dimension of chaotic signal simultaneously. Considering the difficulty in determining the number of...
This paper presents a new approach to short-term wind speed prediction. The chaotic time series analysis method is used to capture the characteristic of complex wind behavior in which a correlation dimension method is employed to calculate embedding dimension of the time series, then a mutual information method is used to determine the time delay. Based on the embedding dimension and time delay, support...
In order to obtain the effective input vector for the prediction of multivariate time series, method of joint entropy determine the dimension(JEDD) is proposed in the reconstructed phase space. For multivariate chaotic time series, Firstly, determine the delay time of each variate with mutual information method, and then propose the algorithm that determines the embedding dimension of phase space...
The chaos-dynamic time delay BP neural network model is built to realize long-term prediction of rock mass displacement of large underground grave, and fast analyze long-time stability of surrounding rock through optimized structure of BP neural network coupling with chaotic-dynamic parameters of displacement. Embedding dimension m is set as the number of input layer, and predicting feedback mode...
Based on the powerful nonlinear mapping ability of support vector machines, the predicting model of support vector machines in combination with takens' delay coordinate phase reconstruction of chaotic time series has been established. Yearly precipitation time series is of the chaotic characters, thus this model is used to try predicting the precipitation. Because of the peculiarity of precipitation...
In classic phase space reconstruction, the time lag is identical. In our research, the different time lags are found more effectively for teletraffic forecasting. In this paper, a method to determine the different time lags in phase space reconstruction is proposed. Simulation results show that the prediction is more accurate by using the different time lags in reconstruction phase space.
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