在ts和window功能中使用每小时数据

本文介绍了在ts和window功能中使用每小时数据的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我有每小时的数据，例如下面的示例，我试图从中创建时间序列并使用window函数.我的最终目标是使用它来训练Arima模型.我很难使用ts()或window()处理日期时间格式.我也尝试使用msts()，但无法使其与日期时间格式一起使用.我已经xts()可以工作了，但是window()或Arima()似乎不能正常工作.

I have hourly data like the sample below that I am trying to create a time-series from and use the window function with. My end goal is to use this to train an Arima model. I'm having a hard time getting ts() or window() to work with my date time format. I've also tried using msts() but couldn't get it to work with the date time format. I have gotten xts() to work, but it doesn't seem to work correctly with the window() or Arima().

是否可以在ts()和window()函数中使用此日期时间格式?任何提示都将不胜感激.

Is it possible to use this date time format with ts() and the window() function? Any tips are greatly appreciated.

代码:

tsData <- ts(SampleData$MedTime[1:24],start='2015-01-01 00:00', frequency=168) train <- window(tsData,end='2015-01-01 15:00')

编辑说明:从提供的最初525个开始，该问题的数据已被截断为仅24个观测值.结果，window()调用也已修改为截断范围内的时间.

Edit Note The data for this problem has been truncated to only 24 observations from the initial 525 provided. As a result, the window() call has been modified as well to a time within the truncated range.

数据:

dput(SampleData[1:24,c("DateTime","MedTime")])

SampleData = structure(list(DateTime = c("2015-01-01 00:00"，"2015-01-01 01:00"， "2015-01-01 02:00"，"2015-01-01 03:00"，"2015-01-01 04:00"，"2015-01-01 05:00"， "2015-01-01 06:00"，"2015-01-01 07:00"，"2015-01-01 08:00"，"2015-01-01 09:00"， "2015-01-01 10:00"，"2015-01-01 11:00"，"2015-01-01 12:00"，"2015-01-01 13:00"， "2015-01-01 14:00"，"2015-01-01 15:00"，"2015-01-01 16:00"，"2015-01-01 17:00"， "2015-01-01 18:00"，"2015-01-01 19:00"，"2015-01-01 20:00"，"2015-01-01 21:00"， "2015-01-01 22:00"，"2015-01-01 23:00")，MedTime = c(11，14， 17，5，5，5.5，8，NA，5.5，6.5，8.5，4，5，9，10，11，7，6，7， 7，5，6，9，9))，.names = c("DateTime"，"MedTime")，row.names = c(NA， 24L)，class ="data.frame")

SampleData = structure(list(DateTime = c("2015-01-01 00:00", "2015-01-01 01:00", "2015-01-01 02:00", "2015-01-01 03:00", "2015-01-01 04:00", "2015-01-01 05:00", "2015-01-01 06:00", "2015-01-01 07:00", "2015-01-01 08:00", "2015-01-01 09:00", "2015-01-01 10:00", "2015-01-01 11:00", "2015-01-01 12:00", "2015-01-01 13:00", "2015-01-01 14:00", "2015-01-01 15:00", "2015-01-01 16:00", "2015-01-01 17:00", "2015-01-01 18:00", "2015-01-01 19:00", "2015-01-01 20:00", "2015-01-01 21:00", "2015-01-01 22:00", "2015-01-01 23:00"), MedTime = c(11, 14, 17, 5, 5, 5.5, 8, NA, 5.5, 6.5, 8.5, 4, 5, 9, 10, 11, 7, 6, 7, 7, 5, 6, 9, 9)), .Names = c("DateTime", "MedTime"), row.names = c(NA, 24L), class = "data.frame")

推荐答案

R中的时间序列

ts()对象具有一些限制.最值得注意的是，它不接受每次观察的时间戳.而是，它请求一个start和freq(end是可选的).此外，freq功能仅限于按季节查看数据.

Time Series in R

The ts() object has a few limitations. Most notably, it doesn't accept time stamps per observation. Instead, it requests a start and freq (the end is optional). Furthermore, the freq capabilities are limited to viewing data in terms of seasons.

Type Frequency Annual 1 Quarterly 4 Monthly 12 Weekly 52

因此，要生成正确的季节"，我们将必须计算每日季节性，其中freq=1440(= 24 * 60).之后，它变得更加复杂.

Thus, to generate the correct "season" we would have to calculate a daily seasonality where freq=1440 (=24*60). It gets a bit more complicated after that.

因此，我强烈建议使用xts或zoo对象创建时间序列.

As a result, I would highly suggest creating the time series with an xts or zoo object.

接下来，出现窗口问题的原因之一是您提供的日期是字符串，而不是 POSIXct 或 POSIXlt 目的.优先考虑.

Next up, one of the reasons for your windowing issues is the date you are supplying is a string and not a POSIXct or POSIXlt object. The prior of which is preferred.

可以找到完整的细分:

as.POSIXct之间的差异/as.POSIXlt和strptime，用于将字符向量转换为POSIXct/POSIXlt

处理R中的时间戳

话虽如此，第一步是将数据从字符格式转换为 POSIXct

With that being said, one of the first steps is to convert your data from character form to POSIXct

# Convert to POSXICT SampleData$DateTime = as.POSIXct(strptime(SampleData$DateTime, format ="%Y-%m-%d %H:%M"))

开窗

从那里开始，如果我们创建xts()对象，则窗口问题变得微不足道.

Windowing

From there, the windowing issue becomes trivial if we create a xts() object.

# install.packages("xts") require(xts) # Create an XTS object to hold the time series sdts = xts(SampleData$MedTime, order.by = SampleData$DateTime) # Subset training train = window(sdts,end= as.POSIXct('2015-01-21 23:00', format ="%Y-%m-%d %H:%M"))