Based on multiple levels of granularity, the notion of sequential three-way granular computing focuses on a multiple stages of thinking, problem-solving, and information processing in threes. This paper interprets, represents, and implements sequential three-way granular computing by a framework of temporal-spatial multi-granularity learning, which is described with the temporality of data and the spatiality of parameters. In real-world decision-making, such a sequential approach is useful to make faster decisions for some objects with the lower cost of decision process and the acceptable accuracy when information is insufficient or unavailable. However, the cost of time-consuming computation for hierarchical multilevel granularity is our concern. To address this issue, we utilize a local strategy to accelerate a sequence of neighborhood-based granulation induced by Gaussian kernel function. Subsequently, local three-way decision rules are investigated based on the Bayesian minimum risk criterion. Moreover, by the construction of a novel local trisection model, we propose a local sequential approach of three-way granular computing under a temporal-spatial multilevel granular structure. Finally, a series of comparative experiments between global and local perspectives is carried out to verify the effectiveness of our proposed models.

基于多个级别的粒度，顺序三向粒度计算的概念侧重于思考，解决问题和信息处理的多个阶段。本文通过时空多粒度学习框架解释，表示和实现顺序三向粒度计算，并以数据的时空性和参数的空间性来描述。在现实世界的决策中，这种顺序方法可用于在信息不足或不可用时以较低的决策过程成本和可接受的准确性对某些对象进行更快的决策。但是，用于分层多级粒度的耗时计算成本是我们关注的问题。为了解决这个问题，我们利用局部策略来加速由高斯核函数引起的基于邻域的造粒序列。随后，基于贝叶斯最小风险准则研究了本地三路决策规则。此外，通过构造新颖的局部三等分模型，我们提出了在时空多级粒度结构下的三向粒度计算的局部顺序方法。最后，在全球和本地视角之间进行了一系列比较实验，以验证我们提出的模型的有效性。我们提出了一种时空多级粒度结构下的三向粒度计算的局部顺序方法。最后，在全球和本地视角之间进行了一系列比较实验，以验证我们提出的模型的有效性。我们提出了一种时空多级粒度结构下的三向粒度计算的局部顺序方法。最后，在全球和本地视角之间进行了一系列比较实验，以验证我们提出的模型的有效性。