This article present a notion of impact-oriented negative sequential rules, in which the left side is a postive sequential pattern or its negation, and the right side is a predefined outcome ot irs negation.
时间序列分析——The main focus of this book is on a systematic development of the theory of sequential hypothesis testing (Part I) and changepoint detection (Part II). In Part III, we briefly describe certain important applications where theoretical results
class torch.nn.Sequential(* args)
一个时序容器。Modules 会以他们传入的顺序被添加到容器中。当然，也可以传入一个OrderedDict。
为了更容易的理解如何使用Sequential, 下面给出了一个例子：
# Example of using Sequential
model = nn.Sequential(
nn.Conv2d(1,20,5),
nn.ReLU(),
nn.Conv2d(20,64,5),
nn
