R语言中时间序列分析及其应用详解

5星 · 超过95%的资源需积分: 19 51 浏览量更新于2024-07-23 收藏 5.44MB PDF 举报

《时间序列分析及其在R中的应用》(Time.Series.Analysis.With.Applications.in.R)是由Jonathan D. Cryer 和 Kung-Sik Chan合著的一本专业书籍。该书聚焦于统计学中的一个重要领域——时间序列分析，特别强调了如何在R编程环境中进行实践操作和应用。时间序列分析是研究随时间变化的数据模式的统计方法，广泛应用于经济、金融、气候、生物医学等多个领域，对预测和理解趋势、季节性以及随机波动具有关键作用。书中涵盖了时间序列分析的基本理论，如平稳性检验、自回归移动平均模型（ARMA）、自回归积分移动平均模型（ARIMA）、季节性分解方法（如季节指数分解法）以及状态空间模型等。此外，作者通过实例展示了如何利用R语言的函数和包，如`forecast`、`ts`和`decomp`，来进行数据预处理、模型构建和结果解释。对于初学者来说，书中可能会介绍时间序列的生成过程，如马尔可夫链和随机过程的基础概念。对于进阶读者，书中可能探讨了动态模型的估计、诊断和选择，以及在金融市场的实证应用，如股票价格、汇率或利率的时间序列分析。此外，还可能涉及时间序列的建模复杂性，如状态空间模型的隐含Markov结构和Kalman滤波器的应用。值得注意的是，R语言作为开源的统计计算平台，其丰富的库和社区支持使得本书成为学习和实践时间序列分析的理想资源。书中不仅提供了理论框架，还提供了实用的代码示例，使读者能够快速掌握并运用到实际工作中。《Time.Series.Analysis.With.Applications.in.R》是一本结合理论深度和实践操作的教材，它对于那些希望在R环境中深入理解并应用时间序列分析技术的专业人士和学生来说，是一本不可或缺的参考书籍。通过阅读和实践书中的内容，读者不仅能提升分析技能，还能更好地理解和解决现实世界中复杂的时间序列问题。

1.1 Examples of Time Series 3

The main impression that we obtain from this plot is that there is little if any infor-

mation about this year’s rainfall amount from last year’s amount. The plot shows no

“trends” and no general tendencies. There is little correlation between last year’s rainfall

amount and this year’s amount. From a modeling or forecasting point of view, this is not

a very interesting time series!

An Industrial Chemical Process

As a second example, we consider a time series from an industrial chemical process.

The variable measured here is a color property from consecutive batches in the process.

Exhibit 1.3 shows a time series plot of these color values. Here values that are neighbors

in time tend to be similar in size. It seems that neighbors are related to one another.

Exhibit 1.3 Time Series Plot of Color Property from a Chemical Process

> win.graph(width=4.875, height=2.5,pointsize=8)

> data(color)

> plot(color,ylab='Color Property',xlab='Batch',type='o')

This can be seen better by constructing the scatterplot of neighboring pairs as we

did with the first example.

Exhibit 1.4 displays the scatterplot of the neighboring pairs of color values. We see

a slight upward trend in this plot—low values tend to be followed in the next batch by

low values, middle-sized values tend to be followed by middle-sized values, and high

values tend to be followed by high values. The trend is apparent but is not terribly

strong. For example, the correlation in this scatterplot is about 0.6.

●

●●

●

●●

●

●●

●

●●

●

Batch

Color Property

0 5 10 15 20 25 30 35

65 75 85

6 Introduction

Monthly Average Temperatures in Dubuque, Iowa

The average monthly temperatures (in degrees Fahrenheit) over a number of years

recorded in Dubuque, Iowa, are shown in Exhibit 1.7.

Exhibit 1.7 Average Monthly Temperatures, Dubuque, Iowa

> win.graph(width=4.875, height=2.5,pointsize=8)

> data(tempdub); plot(tempdub,ylab='Temperature',type='o')

This time series displays a very regular pattern called seasonality. Seasonality for

monthly values occurs when observations twelve months apart are related in some man-

ner or another. All Januarys and Februarys are quite cold but they are similar in value

and different from the temperatures of the warmer months of June, July, and August, for

example. There is still variation among the January values and variation among the June

values. Models for such series must accommodate this variation while preserving the

similarities. Here the reason for the seasonality is well understood—the Northern

Hemisphere’s changing inclination toward the sun.

Monthly Oil Filter Sales

Our last example for this chapter concerns the monthly sales to dealers of a specialty oil

filter for construction equipment manufactured by John Deere. When these data were

first presented to one of the authors, the manager said, “There is no reason to believe

that these sales are seasonal.” Seasonality would be present if January values tended to

be related to other January values, February values tended to be related to other Febru-

ary values, and so forth. The time series plot shown in Exhibit 1.8 is not designed to dis-

play seasonality especially well. Exhibit 1.9 gives the same plot but amended to use

meaningful plotting symbols. In this plot, all January values are plotted with the charac-

ter J, all Februarys with F, all Marches with M, and so forth.

†

With these plotting sym-

bols, it is much easier to see that sales for the winter months of January and February all

tend to be high, while sales in September, October, November, and December are gener-

●

●●

●

Time

Temperature

1964 1966 1968 1970 1972 1974 1976

10 30 50 70

剩余500页未读，继续阅读

yanyanfudi

粉丝: 0
资源: 1

R语言中时间序列分析及其应用详解

Time.Series.Analysis.With.Applications.in.R-Jonathan

Time Series Analysis With Applications in R.zip

基于R语言开发时间序列预测 Time Series Analysis With Applications in R 学习笔记

Time Series Analysis with Applications in R, Second Edition

Time series analysis and its applications with R examples

Times Series Analysis with Applications in R

Time Series Analysis and Its Applications With R Examples 4th 2017

Multivariate Time Series Analysis With R and Financial Applications

Multivariate Time Series Analysis: With R and Financial Applications

Time series analysis and its applications with R examples-时间序列分析及其R应用

最新资源