在 R 中将不规则时间序列更改为规则时间序列 1 分钟答案

【问题标题】：Changing Irregular Time Series to Regular Time series by 1 minutes in R在 R 中将不规则时间序列更改为规则时间序列 1 分钟
【发布时间】：2017-03-06 19:41:03
【问题描述】：

我有一个名为dts 的时间序列，它是不规则的时间序列。

                Date| V1
1994-02-08 01:09:00 | 0.1
1994-02-08 01:18:00 | 0.1
1994-02-08 01:41:00 | 0.1
1994-02-08 01:46:00 | 0.1
1994-02-08 01:50:00 | 0.1
1994-02-08 01:57:00 | 0.1
1994-02-12 20:27:00 | 0.1
1994-02-12 20:28:00 | 0.1
1994-02-12 20:30:00 | 0.1
1994-02-12 20:30:00 | 0.1

我想让它有规律，每 1 分钟安排一次。所需的结果应该是：

                Date| V1
1994-02-08 01:09:00 | 0.1
1994-02-08 01:10:00 | 0.0
1994-02-08 01:11:00 | 0.0
1994-02-08 01:12:00 | 0.0
1994-02-08 01:13:00 | 0.0
1994-02-08 01:14:00 | 0.0
1994-02-08 01:15:00 | 0.0
1994-02-08 01:16:00 | 0.0
1994-02-08 01:17:00 | 0.0
1994-02-08 01:18:00 | 0.1

我想让它变得有规律，以便在规律的时间序列上应用移动平均函数。任何帮助将不胜感激。谢谢！

【问题讨论】：

我猜你在所需结果中的数据时间戳应该每次只增加 1 分钟，而不是从 1994-02-08 01:14 任意跳跃 4 天： 00 到 1994-02-12 01:15:00？我在下面的回答假设

标签： r time xts series zoo

【解决方案1】：

library(xts)

# Create evenly spaced timestamps at frequency you desire (keep it simple to 1 hour here):
ts.1min <- seq(as.POSIXct("2016-10-12 12:00:00"), as.POSIXct("2016-10-12 13:00:00"), by = paste0("60 s"))

# Generate your data for 10 random timestamps in the 1 hour interval:
set.seed(1)
x.toy <- xts(round(rnorm(10, mean = 0, sd = 0.2), 1), 
             ts.1min[sample(x = 1:length(ts.1min), size = 10, replace = FALSE)], dimnames = list(NULL, "V1"))

# > x.toy
# V1
# 2016-10-12 12:00:00  0.1
# 2016-10-12 12:07:00  0.3
# 2016-10-12 12:12:00  0.0
# 2016-10-12 12:15:00  0.1
# 2016-10-12 12:17:00 -0.1
# 2016-10-12 12:20:00  0.1
# 2016-10-12 12:21:00 -0.2
# 2016-10-12 12:38:00 -0.2
# 2016-10-12 12:46:00  0.1
# 2016-10-12 12:57:00 -0.1


# Fill timestamps with no value as 0:
x.res <- merge(x.toy, xts(, ts.1min), fill = 0)

# > head(x.res, 10)
# V1
# 2016-10-12 12:00:00 0.1
# 2016-10-12 12:01:00 0.0
# 2016-10-12 12:02:00 0.0
# 2016-10-12 12:03:00 0.0
# 2016-10-12 12:04:00 0.0
# 2016-10-12 12:05:00 0.0
# 2016-10-12 12:06:00 0.0
# 2016-10-12 12:07:00 0.3
# 2016-10-12 12:08:00 0.0
# 2016-10-12 12:09:00 0.0

【讨论】：