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In this paper, a dynamic model for online time series prediction is proposed, namely kernel general loss algorithm based on evolving participatory learning (EPL-KGLA). The algorithm can develop its structure and parameters autonomously in response to complex environments, capturing the dynamic changes of time series and achieving accurate prediction. Specifically, EPL based on evolving fuzzy systems is employed in recursive clustering to fully utilize useful information in data streams and generate/prune structures to ensure compactness and reduce computational burden. Then, the general loss function is combined with online kernel learning to propose KGLA for updating consequent parameters in real-time, capturing the dynamic features of data streams and avoiding the negative effects of large anomalies or complex noise on model performance, thereby improving prediction accuracy. Finally, simulation experiments on a benchmark dataset and two real-world datasets are verified the robustness of EPL-KGLA. [ABSTRACT FROM AUTHOR]

With the boom of big data, the Internet contains more and more personal behavior information, but it is difficult to extract effectively. A model involving multivariate processing capability must be constructed to deal with these time series with complex characteristics. In this paper, a novel hybrid model embedding Baidu Search Index is therefore proposed to implement multi-step ahead subway passenger flow forecasting. Firstly, we collect data from informative Baidu Search Index, reduce dimensionality, and screen out the powerful predictors by statistical analysis. Secondly, we extract matching common modes at similar time scales between the subway passenger flow and screened Baidu Search Index via multivariate mode decomposition being optimized by multi-objective algorithm. Furthermore, to eliminate pseudo statistical causality, we select the optimal combination of modal components between subway passenger flow and its corresponding Baidu Search Index at each time scale by an innovative multi–modal analysis strategy. Thirdly, we reconstruct the forecasting values of each selected optimal combination as the final results. The empirical results of Beijing, Shanghai and Guangzhou show that the proposed model can significantly outperform six benchmark models in both the level and directional accuracy. So introducing Baidu Search Index creates a sound opportunity to enhance the subway passenger flow forecasting ability. [ABSTRACT FROM AUTHOR]