R语言时间序列分析入门指南

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"ALittleBookofRForTimeSeries" 是一本介绍如何在R软件中进行时间序列分析的小册子，由Avril Coghlan撰写。它提供了很多实例，适合统计学学习者，涵盖了从安装R和R包，到时间序列的基本操作、可视化、分解、指数平滑预测以及ARIMA模型等内容。在R中进行时间序列分析是统计和预测工作中至关重要的一部分，尤其对于处理具有时间依赖性的数据如经济指标、股票价格、气象数据等。以下是对小册子主要内容的详细解释： 1. **如何安装R**：R是一款开源的统计分析软件，用户需要首先从官方网站下载并安装R的基础环境，然后根据需求安装相关的R包以扩展其功能。 2. **R的初步介绍**：R提供了一个交互式的环境，用户可以通过命令行输入指令来执行各种统计计算。它有丰富的图形和数据分析工具，并支持编程，使得数据分析过程可重复且易于理解。 3. **安装R包**：R包是扩展R功能的库，时间序列分析需要用到如`ts`, `forecast`, `tseries`等包，用户可以通过`install.packages()`函数进行安装。 4. **运行R**：启动R后，用户可以输入R命令来加载数据、进行计算或绘制图表。 5. **时间序列分析**：时间序列数据是一系列按照时间顺序排列的数据点。分析包括数据的描述性统计、趋势检测、季节性分析等。 6. **读取时间序列数据**：R通过`ts`函数可以将数据转化为时间序列对象，数据可以从CSV、Excel或其他格式导入。 7. **绘制时间序列**：`plot.ts()`函数用于绘制时间序列图，帮助用户直观地理解数据的动态变化。 8. **时间序列分解**：包括趋势成分、季节性成分和随机成分的分解，例如使用STL（Seasonal and Trend decomposition using Loess）方法。 9. **指数平滑预测**：是一种简单易用的预测方法，包括简单指数平滑、Holt线性趋势平滑和Holt-Winters季节性平滑等，适用于短期预测。 10. **ARIMA模型**：自回归整合滑动平均模型，是时间序列预测的常用工具，能处理非平稳数据，包含AR（自回归）、I（差分）和MA（滑动平均）三个部分。 11. **链接和进一步阅读**：作者提供了更多关于R和时间序列分析的学习资源，供读者深入研究。这本小册子不仅是初学者的入门指南，也为有经验的R用户提供了实践时间序列分析的参考。通过阅读和实践，读者能够掌握在R中进行基本和进阶的时间序列分析技能。

A Little Book of R For Time Series, Release 0.1

> mynames <- c("Mary", "John", "Ann", "Sinead", "Joe", "Mary", "Jim", "John", "Simon")

> table(mynames)

mynames

Ann Jim Joe John Mary Simon Sinead

1 1 1 2 2 1 1

We can store the table variable produced by the function table(), and call the stored table “mytable”, by typing:

> mytable <- table(mynames)

To access elements in a table variable, you need to use double square brackets, just like accessing elements in a

list. For example, to access the fourth element in the table mytable (the number of children called “John”), we

type:

> mytable[[4]]

[1] 2

Alternatively, you can use the name of the fourth element in the table (“John”) to ﬁnd the value of that table

element:

> mytable[["John"]]

[1] 2

Functions in R usually require arguments, which are input variables (ie. objects) that are passed to them, which

they then carry out some operation on. For example, the log10() function is passed a number, and it then calculates

the log to the base 10 of that number:

> log10(100)

In R, you can get help about a particular function by using the help() function. For example, if you want help

about the log10() function, you can type:

> help("log10")

When you use the help() function, a box or webpage will pop up with information about the function that you

asked for help with.

If you are not sure of the name of a function, but think you know part of its name, you can search for the function

name using the help.search() and RSiteSearch() functions. The help.search() function searches to see if you

already have a function installed (from one of the R packages that you have installed) that may be related to some

topic you’re interested in. The RSiteSearch() function searches all R functions (including those in packages that

you haven’t yet installed) for functions related to the topic you are interested in.

For example, if you want to know if there is a function to calculate the standard deviation of a set of numbers, you

can search for the names of all installed functions containing the word “deviation” in their description by typing:

> help.search("deviation")

Help files with alias or concept or title matching

’deviation’ using fuzzy matching:

genefilter::rowSds

Row variance and standard deviation of

a numeric array

nlme::pooledSD Extract Pooled Standard Deviation

stats::mad Median Absolute Deviation

stats::sd Standard Deviation

vsn::meanSdPlot Plot row standard deviations versus row

1.5. A brief introduction to R 9

A Little Book of R For Time Series, Release 0.1

Among the functions that were found, is the function sd() in the “stats” package (an R package that comes with

the standard R installation), which is used for calculating the standard deviation.

In the example above, the help.search() function found a relevant function (sd() here). However, if you did not

ﬁnd what you were looking for with help.search(), you could then use the RSiteSearch() function to see if a search

of all functions described on the R website may ﬁnd something relevant to the topic that you’re interested in:

> RSiteSearch("deviation")

The results of the RSiteSearch() function will be hits to descriptions of R functions, as well as to R mailing list

discussions of those functions.

We can perform computations with R using objects such as scalars and vectors. For example, to calculate the

average of the values in the vector myvector (ie. the average of 8, 6, 9, 10 and 5), we can use the mean() function:

> mean(myvector)

[1] 7.6

We have been using built-in R functions such as mean(), length(), print(), plot(), etc. We can also create our own

functions in R to do calculations that you want to carry out very often on different input data sets. For example,

we can create a function to calculate the value of 20 plus square of some input number:

> myfunction <- function(x) { return(20 + (x

x)) }

This function will calculate the square of a number (x), and then add 20 to that value. The return() statement

returns the calculated value. Once you have typed in this function, the function is then available for use. For

example, we can use the function for different input numbers (eg. 10, 25):

> myfunction(10)

[1] 120

> myfunction(25)

[1] 645

To quit R, type:

> q()

1.6 Links and Further Reading

Some links are included here for further reading.

For a more in-depth introduction to R, a good online tutorial is available on the “Kickstarting R” website, cran.r-

project.org/doc/contrib/Lemon-kickstart.

There is another nice (slightly more in-depth) tutorial to R available on the “Introduction to R” website, cran.r-

project.org/doc/manuals/R-intro.html.

1.7 Acknowledgements

For very helpful comments and suggestions for improvements on the installation instructions, thank you very

much to Friedrich Leisch and Phil Spector.

1.8 Contact

I will be very grateful if you will send me (Avril Coghlan) corrections or suggestions for improvements to my

email address a.coghlan@ucc.ie

10 Chapter 1. How to install R

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R语言时间序列分析入门指南

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