Feature mining is a challenging work in the field of multivariate time series (MTS) data mining. Traditional methods suffer from three major issues. 1) Learned shapelets may seriously diverge from original subsequences since learning methods do not restrain the learned ones similar to raw sequences, which reduces interpretability. 2) Existing rule mining methods just generate association rules based on feature combination of different variables without considering temporal relations among features, which could not adequately express the essential characteristics of MTS data. 3) Most deep learning methods only mine global and high-level features of MTS data, which affects interpretability. To address these issues, we propose a temporal association rule mining method based on Generative Adversarial Network (GAN) called TAR-GAN. First, a shapelet mining method based on GAN (SGAN) is advanced to discover dataset-level and sample-level shapelets of all variables in MTS data. Second, a Temporal Graph based Rule Mining method (TGRM) is introduced to discover temporal association rules based on the temporal relationships among shapelets of different variables. Meanwhile, a Fast Convolution-based Similarity Measure methods(FCSM) is introduced to measure the similarity between MTS samples and temporal association rules. Furthermore, an adversarial training strategy is introduced to ensure the effectiveness and stability of generated temporal association rules, which could reflect the essential characteristics of MTS data. Extensive experiments on 12 datasets show the effectiveness and efficiency of our method.

Recently, rule-based classification on multivariate time series (MTS) data has gained lots of attention, which could improve the interpretability of classification. However, state-of-the-art approaches suffer from three major issues. 1) few existing studies consider temporal relations among features in a rule, which could not adequately express the essential characteristics of MTS data. 2) due to the concept drift and time warping of MTS data, traditional methods could not mine essential characteristics of MTS data. 3) existing online learning algorithms could not effectively update shapelet-based temporal association rules of MTS data due to its temporal relationships among features of different variables. To handle these issues, we propose an online learning method for temporal association rule on dynamically collected MTS data (OTARL). First, a new type of rule named temporal association rule is defined and mined to represent temporal relationships among features in a rule. Second, an online learning mechanism with a probability correlation-based evaluation criterion is proposed to realize the online learning of temporal association rules on dynamically collected MTS data. Finally, an ensemble classification approach based on maximum-likelihood estimation is advanced to further enhance the classification performance. We conduct experiments on ten real-world datasets to verify the effectiveness and efficiency of our approach.

A theoretical interaction consensus model in group decision making with distributed linguistic trust information is proposed. To do that, the concept of distributed linguists trust function (DLTF) is defined, and then the associated operational laws and aggregation operations are explored. Combing the expectation degree and uncertainty degrees, a ranking method for distributed linguists trust function is proposed. To identify the inconsistent experts, three levels of consensus degree with DLTF are calculated. After that, a novel feedback mechanism is activated to generate recommendation advices for the inconsistent experts to higher consensus degree. Therefore, the inconsistent experts are able to reach the threshold value of group consensus. Finally, after consensus has been achieved, a ranking order relation for distributed linguists trust functions is constructed to select the most appropriate alternative.