# 时间序列分析101：序言

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鉴于时间序列数据的重要性，打算写一份时间序列的纯小白入门教程，从基本概念，EDA，再到各种模型，希望读者通过学习完这些章节能对时间序列数据分析有一个全面的了解。

某位深度学习的畅销书作者曾说过，“如果你在读材料的时候感到很难理解，大多数时间都不是你的问题，是写材料的人没有花足够多的时间解释理解所有概念的各个小知识点”。这也是我个人在多年的学习生涯中所深刻体会到的，很多国内的教材或材料“不讲人话”，充满了机器语言。这也是我在撰写这份材料的时候尽量避免的，对于这份入门材料，不会看到很多公式的堆砌，尽量用门外汉也能理解的通俗语言来解释说明，自己还没理解的内容不会写进来。

本教程的框架主要基于Aileen Nielsen写的Practical Time Series Analysis这本书，结合了大量其他参考资料，总结补充而成。

**总大纲**

1.概述

2.准备和处理时间序列数据

3.探索式分析(EDA)

4.基于统计学的时间序列分析方法

5.特征生成和特征选择

6.基于机器学习的时间序列分析方法

7.基于深度学习的时间序列分析方法

8.模型评估和性能考虑

**参考资料**

书籍：

1. <https://www.oreilly.com/library/view/practical-time-series/9781492041641/>
2. <https://machinelearningmastery.com/introduction-to-time-series-forecasting-with-python/>

网站：

1. <https://pythondata.com/forecasting-time-series-autoregression/>
2. <https://goodboychan.github.io/python/datacamp/time_series_analysis/2020/06/08/02-Moving-Average-and-ARMA-Models.html>
3. <https://towardsdatascience.com/time-series-forecasting-using-auto-arima-in-python-bb83e49210cd>
4. <https://www.machinelearningplus.com/time-series/vector-autoregression-examples-python/#6testingcausationusinggrangerscausalitytest>
5. <https://towardsdatascience.com/vector-autoregressive-for-forecasting-time-series-a60e6f168c70>
6. <https://towardsdatascience.com/dynamic-time-warping-3933f25fcdd>
7. <http://colah.github.io/posts/2015-08-Understanding-LSTMs/>


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