Causal inference permits us to discover covert relationships of various variables in time series. However, in most existing works, the variables mentioned above are the dimensions. The causality between dimensions could be cursory, which hinders the comprehension of the internal relationship and the benefit of the causal graph to the neural networks (NNs). In this paper, we find that causality exists not only outside but also inside the time series because it reflects a succession of events in the real world. It inspires us to seek the relationship between internal subsequences. However, the challenges are the hardship of discovering causality from subsequences and utilizing the causal natural structures to improve NNs. To address these challenges, we propose a novel framework called Mining Causal Natural Structure (MCNS), which is automatic and domain-agnostic and helps to find the causal natural structures inside time series via the internal causality scheme. We evaluate the MCNS framework and impregnation NN with MCNS on time series classification tasks. Experimental results illustrate that our impregnation, by refining attention, shape selection classification, and pruning datasets, drives NN, even the data itself preferable accuracy and interpretability. Besides, MCNS provides an in-depth, solid summary of the time series and datasets.

提出 Mining Causal Natural Structure (MCNS) 框架，通过内部因果关系方案在时间序列中发现因果关系结构，进而改善神经网络的性能，在时间序列分类任务中通过改进注意力机制、形状选择分类和修剪数据集等方式提高网络的准确性和可解释性，并提供时间序列和数据集的深入可靠总结。

MCNS: 基于新颖内部因果性方案的时间序列中挖掘因果自然结构