December 2022
Intermediate to advanced
376 pages
11h 30m
Chinese
Content preview from 时间序列分析实战：基于机器学习和统计学
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2
｜
第
1
章
•
经济学
•
天文学
我们将看到，这些学科的发展节奏和成果与同时期可用的时间序列数据的本质紧密相关。
1.1.1
医学中的时间序列问题
医学是一个由数据驱动的领域。几个世纪以来，它积累了很多知识，来让我们进行有趣的
时间序列分析。现在，让我们研究医学领域中的几个故事线索，了解它们是如何随时间发
展的。
尽管预后是医学实践的重要组成部分，但是在基于数学的预测研究方面，医学的起步慢得
出奇。有多种原因造成了这种情况。虽然医学发展了几个世纪，但是应用统计学和概率思
维来进行研究是相对较新的做法，之前并不可行。而且，大多数医生独立行医，彼此之间
既缺乏专业交流，也没有用于正式记录患者健康状况和人口健康状况的数据保存和管理方
法。因此，即使将早期的医师训练成统计专家，他们可能也没有数据来得出有效结论。
这并不是要指责早期的医师，而是为了解释在早期的人口健康状况研究方面，为什么应用
时间序列做出创新的是一个服饰经销商，而不是医师。仔细想想，这是有道理的：在早
期，城市里的服饰经销商可能比医师拥有更多的记录，并且更易发现趋势。
这个创新者就是
John
Graunt
，
17
世纪伦敦的一个服饰经销商。
Graunt
对从
16
世纪初以
来伦敦教区保存的死亡记录进行了研究。在研究过程中，他开创了人口统计学。
1662
年，
Graunt
出版了著作《关于死亡率的自然观察和政治观察》
，如图
1-1
所示。
图 1-1：John Graunt 的死亡率统计表是应用时间序列分析思维来解决医学问题的首批研究成果之一。
图片来源：维基百科
在这本书中， ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.