R 简明教程

R - Time Series Analysis

时间序列是一系列数据点，其中每个数据点都与时间戳相关联。一个简单的例子是股票市场中某支股票在给定日期不同时间点的价格。另一个例子是某个地区不同月份的降雨量。R 语言使用许多函数来创建、处理和绘制时间序列数据。时间序列的数据存储在称为 time-series object 的 R 对象中。它也是一个 R 数据对象，如向量或数据框。

Time series is a series of data points in which each data point is associated with a timestamp. A simple example is the price of a stock in the stock market at different points of time on a given day. Another example is the amount of rainfall in a region at different months of the year. R language uses many functions to create, manipulate and plot the time series data. The data for the time series is stored in an R object called time-series object. It is also a R data object like a vector or data frame.

时间序列对象是使用 ts() 函数创建的。

The time series object is created by using the ts() function.

Syntax

时间序列分析中 ts() 函数的基本语法为：

The basic syntax for ts() function in time series analysis is −

timeseries.object.name <-  ts(data, start, end, frequency)

以下是所用参数的描述 -

Following is the description of the parameters used −

  1. data is a vector or matrix containing the values used in the time series.

  2. start specifies the start time for the first observation in time series.

  3. end specifies the end time for the last observation in time series.

  4. frequency specifies the number of observations per unit time.

除了“data”参数之外，所有其他参数都是可选的。

Except the parameter "data" all other parameters are optional.

Example

考虑从 2012 年 1 月开始某个地方的年降雨量详细信息。我们创建了为期 12 个月的时间序列对象并对其进行绘制。

Consider the annual rainfall details at a place starting from January 2012. We create an R time series object for a period of 12 months and plot it.

# Get the data points in form of a R vector.
rainfall <- c(799,1174.8,865.1,1334.6,635.4,918.5,685.5,998.6,784.2,985,882.8,1071)

# Convert it to a time series object.
rainfall.timeseries <- ts(rainfall,start = c(2012,1),frequency = 12)

# Print the timeseries data.
print(rainfall.timeseries)

# Give the chart file a name.
png(file = "rainfall.png")

# Plot a graph of the time series.
plot(rainfall.timeseries)

# Save the file.
dev.off()

当我们执行上述代码时，它会产生以下结果和图表：

When we execute the above code, it produces the following result and chart −

Jan    Feb    Mar    Apr    May     Jun    Jul    Aug    Sep
2012  799.0  1174.8  865.1  1334.6  635.4  918.5  685.5  998.6  784.2
        Oct    Nov    Dec
2012  985.0  882.8 1071.0

时间序列图表：

The Time series chart −

rainfall

Different Time Intervals

函数 ts() 中 frequency 参数的值决定了测量数据点的时段。12 的值表示该时间序列为 12 个月。其他值及其含义如下：

The value of the frequency parameter in the ts() function decides the time intervals at which the data points are measured. A value of 12 indicates that the time series is for 12 months. Other values and its meaning is as below −

  1. frequency = 12 pegs the data points for every month of a year.

  2. frequency = 4 pegs the data points for every quarter of a year.

  3. frequency = 6 pegs the data points for every 10 minutes of an hour.

  4. frequency = 24*6 pegs the data points for every 10 minutes of a day.

Multiple Time Series

我们通过将这两个系列合并到一个矩阵中，可以在一个图表中绘制多个时间序列。

We can plot multiple time series in one chart by combining both the series into a matrix.

# Get the data points in form of a R vector.
rainfall1 <- c(799,1174.8,865.1,1334.6,635.4,918.5,685.5,998.6,784.2,985,882.8,1071)
rainfall2 <-
           c(655,1306.9,1323.4,1172.2,562.2,824,822.4,1265.5,799.6,1105.6,1106.7,1337.8)

# Convert them to a matrix.
combined.rainfall <-  matrix(c(rainfall1,rainfall2),nrow = 12)

# Convert it to a time series object.
rainfall.timeseries <- ts(combined.rainfall,start = c(2012,1),frequency = 12)

# Print the timeseries data.
print(rainfall.timeseries)

# Give the chart file a name.
png(file = "rainfall_combined.png")

# Plot a graph of the time series.
plot(rainfall.timeseries, main = "Multiple Time Series")

# Save the file.
dev.off()

当我们执行上述代码时，它会产生以下结果和图表：

When we execute the above code, it produces the following result and chart −

           Series 1  Series 2
Jan 2012    799.0    655.0
Feb 2012   1174.8   1306.9
Mar 2012    865.1   1323.4
Apr 2012   1334.6   1172.2
May 2012    635.4    562.2
Jun 2012    918.5    824.0
Jul 2012    685.5    822.4
Aug 2012    998.6   1265.5
Sep 2012    784.2    799.6
Oct 2012    985.0   1105.6
Nov 2012    882.8   1106.7
Dec 2012   1071.0   1337.8

多时间序列图表 -

The Multiple Time series chart −

rainfall combined