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Due to the existence of observation noise such as multi-path error, the accuracy of deformation prediction results are affected by using the data series of GNSS deformation monitoring. In order to examine the influence of measurement error on the deformation prediction results, the GNSS derived surface displacement time series of Miaodian landslide in Jingyang Area of Shaanxi, and those after noise suppression in combination with chaos theory were analyzed. Firstly, the mutual information method was used to determine the time delay of the surface displacement time series, and the Cao method was used to determine the embedding dimension to obtain the phase space reconstruction parameters. Secondly, the maximum Lyapunov exponent method was used to identify the chaotic characteristics of the two surface displacement time series. Finally, the weighted first-order local prediction method, the largest Lyapunov exponent prediction method, and the BP neural network prediction method were used to predict the landslide surface displacements. The results show that the GNSS landslide surface displacement time series and the time series after noise suppression have chaotic characteristics. The BP neural network prediction method has good prediction performance with an MAE of 0.4 mm and an MRE of 11.9%. After S-transform noise suppression, the MAE and MRE are 0.1 mm and 4.12%, respectively. Compared with the original time series, the prediction performance has been significantly improved after noise suppression. [ABSTRACT FROM AUTHOR]

A chaotic parameter-optimized radial basis function (RBF) forecasting model was proposed. The chaos theory was used to obtain the embedded dimension and delay time of the ENPEMF， and the obtained parameters were used to optimize the RBF neural network. Finally， the trained optimized-RBF was utilized to forecast the strength trend of 14 d ENPEMF data. Numerical simulation results show that the improved RBF model could forecast the Rossler time series well with small error. Applying the improved RBF algorithm to the ENPEMF data before Ms7.0 earthquake in Lushan， it can effectively forecast the ENPEMF intensity trend 14 d before earthquake. The forecasting effect and accuracy are significantly better than that of the traditional RBF algorithm， which is expected to provide support for electromagnetic monitoring and analysis before earthquakes and geological disasters. [ABSTRACT FROM AUTHOR]

In order to investigate the evaluation of neuronal firing time series with time, this paper proposed an improved visibility graph method called local visibility graph method for constructing complex networks from time series, which could map time-domain of neuronal firing time series into network domain. By constructing a complex network topology and calculating its statistical properties in a sliding time window, the method described the time-varying statistical characteristics of neuronal firing time series. As a paradigm, three time series of different bursting modes generated by map neuronal model were used to construct networks, realize visualize topology and analyze statistical characteristics. The results show that network topology, average path length and clustering coefficient can depict time-varying morphological characteristics of the time series. The three properties show parameter sensitivity to neuronal bursting. There is a correlation between sparse degree of neuronal bursting and the community size in the complex networks. Evolution characteristics of neuronal firing time series in network-domain can depict their time-domain features, and the ideology proposed in this paper provides a new way for on-line neural signal processing. [ABSTRACT FROM AUTHOR]

A chaotic time series hybrid forecasting method based on wavelet transform, least squares support vector machine (LSSVM) optimized by particle swarrn optimization (PSO) and generalized au-toregressive conditional heteroscedasticity (GARCH) is proposed. Firstly, the chaotic time series is decomposed and reconstructed into approximate series and detailed series. Secondly, the approximate series future values are predicted by PSO-LSSVM model; while the detailed series future values are forecasted by GARCH model. Finally, the sum of the approximate and detailed series future values is used as the final forecasting values. Two chaotic time series, namely, Mackey-Glass and variable-parameter Logistic, are used to evaluate the performance. The results imply that the proposed method can provide more accurate results, which demonstrates the validity of this method. [ABSTRACT FROM AUTHOR]