时间序列数据操作 [英] Time-Series Data manipulation

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本文介绍了时间序列数据操作的处理方法,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

任何人都可以推荐用于时间序列数据操作的资源.我不是在寻找时间序列统计分析(例如 ARIMA、Forcasting 等).相反,我想根据时间段提取一部分数据.

Can anybody suggest resources for time-series data manipulation. I'm not looking for time-series statistical analysis (e.g. ARIMA,Forcasting, etc). Instead, I want to extract a portion of data based on a time segment.

感谢德克 &莫森!@Dirk:我肯定会尝试动物园.我听说这对 TS 有好处,但出于某种原因,我从脑海中溜走了.@Mohsen:我没有使用分解方法.但我试过stl,&它给了我很多错误.我希望我能得到更多的细节.另外,我寻找了你提供给我的链接.但这正在使用其他软件在 TS 上工作.我对 TS 的统计分析没有问题.但是我在 R 中的 TS 数据操作中遇到了问题.

Thanks Dirk & Mohsen! @Dirk: I'll definitely try zoo. I heard that it's good for TS, but for some reason just slip out of my mind. @Mohsen: I didn't use decomposition method. But I tried stl, & it gives me lot of errors. I wish I can get more details on it.Also, I looked for the link that you provided me. But that is working on TS using other software. I don't have problem with statistical analysis with TS. But I'm having problem in TS data manipulation in R.

此外,我大部分时间都在处理日常、每周和每月数据.但我遇到的例子是年度数据.所以,当我尝试复制我的数据集中的例子时,我得到了很多错误.我无法格式化每天、每周和每月统计.例如.我想要每周格式的以下代码.但是当我把日期放在开始"部分时,它给了我错误.这就是为什么我正在寻找一些仅提供有关时间序列数据操作示例的资源的原因.所有类型的操作.一旦我可以从时间序列中提取所需的数据,我就可以运行统计分析.

Also, majority of the time I deal with daily,weekly & monthly data. But the examples that I come across is yearly data. So, whn I try to replicate the examples in my dats set I get set I get lots of errors. i can't able to format the daily,weekly & monthly stats. E.g. I want the following code in weekly format. But when I put the date in "start" segment, it gives me error. That's why I'm looking for some resources which gives examples only on time-series data manipulation All kind of manipulation. Once I can extract required data in time-series fromat I can run statistical analysis.

数据<-ts(data[,1],start=1956,freq=12)

data<-ts(data[,1],start=1956,freq=12)

推荐答案

请看zoo 包含许多子集和聚合操作的包.

Please look at the documentation of the zoo package which comprises a number of subsetting and aggregation operations.

这篇关于时间序列数据操作的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!

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