预测时间序列数据

Question

           **Point Forecast     Lo 80    Hi 80       Lo 95    Hi 95 account
2017.4137        180.8861 103.03618 258.7361  61.8249012 299.9474  R00172
2017.4164        181.3260 102.63465 260.0173  60.9779946 301.6739  R00172
2017.4192        181.7658 102.24090 261.2906  60.1429756 303.3886  R00172
2017.4219        182.2056 101.85468 262.5565  59.3194730 305.0917  R00172
2017.4247        182.6454 101.47575 263.8151  58.5071341 306.7837  R00172
2017.4274        183.0852 101.10391 265.0665  57.7056233 308.4648  R00172**

以下输出如下 我的要求是转换这个2017.4137,2017.4164,2017.4192......... 转换成日期格式。

 esw<-rownames(df2)
> esw
  [1] "2017.4137"   "2017.4164"   "2017.4192"   "2017.4219"   "2017.4247"   "2017.4274"  
  [7] "2017.4301"   "2017.4329"   "2017.4356"   "2017.4384"   "2017.4411"   "2017.4438"  
 [13] "2017.4466"   "2017.4493"   "2017.4521"   "2017.4548"   "2017.4575"   "2017.4603"  
 [19] "2017.4630"   "2017.4658"   "2017.4685"   "2017.4712"   "2017.4740"   "2017.4767"  
 [25] "2017.4795"   "2017.4822"   "2017.4849"   "2017.4877"   "2017.4904"   "2017.4932"  

我无法获取日期格式

df2$dates<-as.Date(esw,format="%Y/%m/%d")

df2$dates<-as.Date(esw,format="%Y/%m/%d")

> df2$dates
  [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
 [31] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
 [61] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
 [91] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 

我得到了所有的 NA。 任何人都可以帮助我如何进一步进行吗？

谢谢

Answer 1

以下是如何使用 lubridate 将 esw 转换为日期时间格式：

esw <- c("2017.4137",   "2017.4164",   "2017.4192" ,  "2017.4219",   "2017.4247",   "2017.4274",  
                 "2017.4301",   "2017.4329",   "2017.4356" ,  "2017.4384" ,  "2017.4411" ,  "2017.4438",  
                  "2017.4466",   "2017.4493",   "2017.4521",   "2017.4548",   "2017.4575" ,  "2017.4603" , 
                  "2017.4630",   "2017.4658",   "2017.4685" ,  "2017.4712" ,  "2017.4740" ,  "2017.4767" , 
                  "2017.4795",   "2017.4822",   "2017.4849" ,  "2017.4877" ,  "2017.4904" ,  "2017.4932")
esw <- as.numeric(esw)
library(lubridate)
format(date_decimal(esw), "%Y-%m-%d %H:%M:%S")

 [1] "2017-06-01 00:00:43" "2017-06-01 23:39:50" "2017-06-03 00:11:31" "2017-06-03 23:50:38" "2017-06-05 00:22:19"
 [6] "2017-06-06 00:01:26" "2017-06-06 23:40:33" "2017-06-08 00:12:14" "2017-06-08 23:51:21" "2017-06-10 00:23:02"
[11] "2017-06-11 00:02:09" "2017-06-11 23:41:16" "2017-06-13 00:12:57" "2017-06-13 23:52:04" "2017-06-15 00:23:45"
[16] "2017-06-16 00:02:52" "2017-06-16 23:41:59" "2017-06-18 00:13:40" "2017-06-18 23:52:47" "2017-06-20 00:24:28"
[21] "2017-06-21 00:03:35" "2017-06-21 23:42:43" "2017-06-23 00:14:23" "2017-06-23 23:53:31" "2017-06-25 00:25:11"
[26] "2017-06-26 00:04:19" "2017-06-26 23:43:26" "2017-06-28 00:15:07" "2017-06-28 23:54:14" "2017-06-30 00:25:55"

如果您只需要日期而不需要时间，请小心，因为年份分数可能会导致日期重复。在这种情况下，2017 年 6 月 1 日

format(date_decimal(esw), "%Y-%m-%d")
 [1] "2017-06-01" "2017-06-01" "2017-06-03" "2017-06-03" "2017-06-05" "2017-06-06" "2017-06-06" "2017-06-08" "2017-06-08"
[10] "2017-06-10" "2017-06-11" "2017-06-11" "2017-06-13" "2017-06-13" "2017-06-15" "2017-06-16" "2017-06-16" "2017-06-18"
[19] "2017-06-18" "2017-06-20" "2017-06-21" "2017-06-21" "2017-06-23" "2017-06-23" "2017-06-25" "2017-06-26" "2017-06-26"
[28] "2017-06-28" "2017-06-28" "2017-06-30"