Recently, the advancement of cognitive computing and three-way decisions has enabled in-depth sequential pattern understanding through temporal association analysis. The main challenge is to obtain concise patterns that express richer semantics for multivariate time series (MTS) analysis. In this paper, we propose a tri-partition state alphabet-based sequential pattern (Tri-SASP) for MTSs. First, a tri-wildcard gap inserted between each pair of adjacent states enhances the flexibility of the method. Second, a given set of states is partitioned into positive (POS), negative (NEG) and boundary (BND) regions. The states in POS can only be used to construct a Tri-SASP, the states in NEG can only be matched by a tri-wildcard gap, and the states in BND can be used in both ways. Finally, horizontal and vertical algorithms are proposed to obtain frequent Tri-SASPs in a breadth-first manner. The experimental results on four real-world datasets show that (1) the discovered Tri-SASPs and temporal rules can enrich human cognition; (2) the two tri-partition strategies can bring us very meaningful and varied Tri-SASPs; and (3) the two algorithms are effective and scalable.

近来，认知计算和三向决策的发展已经通过时间关联分析实现了对顺序模式的深入理解。主要挑战是要获得简洁的模式，以表达更丰富的语义，以进行多变量时间序列（MTS）分析。在本文中，我们提出了一种用于MTS的基于三分区状态字母的顺序模式（Tri-SASP）。首先，在每对相邻状态之间插入三通配符间隙可增强该方法的灵活性。其次，将给定的状态集划分为正（POS），负（NEG）和边界（BND）区域。POS中的状态只能用于构造Tri-SASP，NEG中的状态只能与三通配符间隙匹配，BND中的状态可以两种方式使用。最后，提出了水平和垂直算法，以广度优先的方式获得频繁的Tri-SASP。在四个真实世界的数据集上的实验结果表明：（1）发现的Tri-SASP和时间规则可以丰富人类的认知；（2）两种三分区策略可以为我们带来非常有意义且多样化的Tri-SASP；（3）这两种算法是有效且可扩展的。