November 2023
Intermediate to advanced
512 pages
11h 53m
Chinese
Content preview from 利用 Python 进行数据分析：原书第 3 版
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331
第 11 章
时间序列
时间序列数据是一种重要的结构化数据形式，应用于多个领域，包括金融学、经济学、
生态学、神经科学、物理学等。在多个时间点重复记录的值就形成了时间序列。很多时
间序列是固定频率的，也就是说，数据点是根据某种规则定期出现的，比如每
15
秒、
每
5
分钟或每月一次。时间序列也可以是不定期的，没有固定的时间单位或单位之间的
偏移量。如何标记和引用时间序列数据取决于具体的应用，主要有以下几种：
时间戳
特定的时刻。
固定的周期
如
2017
年
1
月整月或
2020
年全年。
时间区间
由起始时间戳和结束时间戳表示。周期可以看作时间区间的特例。
试验时间或运行时间
每个时间戳都是相对于特定起始时间的度量（例如，饼干放入烤箱后每秒烘烤的直
径 ）， 从
0
开始。
本章主要讲解前三种时间序列。许多技术都可用于处理试验时间序列，其索引可能是整
数或浮点数，表示距试验起始时刻已经过去的时间。最简单的时间序列是按时间戳索
引的。
pandas
也支持基于时间差的索引，它可以有效表示试验时间或运行时间。本
书不涉及时间差索引，请参考
pandas
的文档（
http://pandas.pydata.org/
）学
习更多内容。
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