Combining time-series evidence: A complex network model based on a visibility graph and belief entropy

Combining basic probability assignments (BPAs) with time series is common in real-life cases. Therefore, a new evidence fusion approach based on belief entropy and a visibility graph (BE-VG) is proposed. The approach converts a time-series BPA into a weighted visibility graph (WVG). In addition, some numerical examples are illustrated to illustrate the efficiency and applicability of the proposed method. Finally, to demonstrate the effect of the BE-VG method, the proposed method is applied to electroencephalogram (EEG) dynamic fusion. Experimentally, the results indicate that the BE-VG method is effective and accurate in conducting EEG signal fusion.