A novel BEMD-based method for forecasting tourist volume with search engine data

As helpful big data, search engine data (SED) regarding tourism-related factors have currently been introduced to tourist volume prediction, but they have been shown to impact the tourism market on different timescales (or frequency band). This study develops a novel forecasting method using an emerging multiscale analysis—bivariate empirical mode decomposition (BEMD)—to investigate multiscale relationships. Three major steps are performed: (1) SED process to construct an informative index from sufficient SED using statistical analyses, (2) multiscale analysis to extract scale-aligned common factors from the bivariate data of tourist volumes and SED using BEMD, and (3) tourist volume prediction using an SED-based index. In the empirical study, the novel BEMD-based method with SED is used to forecast the tourist volume of Hainan in China, a global tourist attraction, and significantly outperforms both popular techniques (not considering SED or multiscales) and similar variants (considering SED or multiscales) in accuracy and robustness.

Automated Classification of Focal and Non-Focal EEG Signals Based on Bivariate Empirical Mode Decomposition

The chapter presents a new approach of computer aided diagnosis of focal electroencephalogram (EEG) signals by applying bivariate empirical mode decomposition (BEMD). Firstly, the focal and non-focal EEG signals are decomposed using the BEMD, which results in intrinsic mode functions (IMFs) corresponding to each signal. Secondly, bivariate bandwidths namely, amplitude bandwidth, precession bandwidth, and deformation bandwidth are computed for each obtained IMF. Interquartile range (IQR) values of bivariate bandwidths of IMFs are employed as the features for classification. In order to perform classification least squares support vector machine (LS-SVM) is used. The results of the experiment suggest that the computed bivariate bandwidths are significantly useful to discriminate focal EEG signals. The resultant classification accuracy obtained using proposed methodology, applied on the Bern-Barcelona EEG database, is 84.01%. The obtained results are encouraging and the proposed methodology can be helpful for identification of epileptogenic focus.

Coupling Fault Feature Extraction Method Based on Bivariate Empirical Mode Decomposition and Full Spectrum for Rotating Machinery

To accurately extract the fault characteristics of vibration signals of rotating machinery is of great significance to the unit online monitoring and evaluation. However, because the current feature extraction methods are mainly for single channel, the results of feature extraction are often inaccurate. To this end, a coupling fault feature extraction method based on bivariate empirical mode decomposition (BEMD) and full spectrum is proposed for rotating machinery. Firstly, the two-dimensional orthogonal signal obtained by orthogonal sampling technique is decomposed by bivariate empirical mode decomposition to obtain the intrinsic mode function with phase information. In order to obtain the sensitive modal components, the sensitivity coefficients are constructed on the basis of mutual information. Then, the sensitivity coefficient of each intrinsic mode function is calculated, and the intrinsic mode function with the larger sensitive coefficient is selected as the sensitive component. Finally, the full spectrum of the sensitive component is obtained using the full vector envelope technique, so as to get a comprehensive and accurate characteristic component. The results of simulations experiment and an application example show that this method can extract the fault characteristic component of the rotating machinery comprehensively and accurately. It is of great significance to realize the accurate diagnosis of coupling faults of rotating machinery.