我有一些类似于以下的时间序列数据：
AAPL.Close MSFT.Close GOOG.Close INTC.Close NVDA.Close
2020-01-06 299.80 159.03 1394.21 59.93 237.060
2020-01-07 298.39 157.58 1393.34 58.93 239.930
2020-01-08 303.19 160.09 1404.32 58.97 240.380
数据已经是xts格式，并且具有以下结构：
An ‘xts’ object on 2020-01-06/2020-04-03 containing:
Data: num [1:63, 1:5] 300 298 303 310 310 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr [1:5] "AAPL.Close" "MSFT.Close" "GOOG.Close" "INTC.Close" ...
Indexed by objects of class: [POSIXct,POSIXt] TZ:
xts Attributes:
List of 4
$ from : chr "20200105 18:04:21"
$ to : chr "20200405 18:04:21"
$ src : chr "IB"
$ updated: POSIXct[1:1], format: "2020-04-05 19:04:22.20857"
但是，我要放入的结构与以下内容紧密相关：
An ‘xts’ object on 2020-01-06/2020-04-02 containing:
Data: num [1:62, 1:5] 300 298 303 310 310 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr [1:5] "AAPL.Close" "MSFT.Close" "GOOG.Close" "INTC.Close" ...
Indexed by objects of class: [Date] TZ: UTC
xts Attributes:
List of 2
$ src : chr "yahoo"
$ updated: POSIXct[1:1], format: "2020-04-05 16:30:35"
当我尝试以下操作时：
dat2 <- xts(dat, order.by = as.Date(time(dat)))
结构将变成这个：
An ‘xts’ object on 2020-01-05/2020-04-02 containing:
Data: num [1:63, 1:5] 300 298 303 310 310 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr [1:5] "AAPL.Close" "MSFT.Close" "GOOG.Close" "INTC.Close" ...
Indexed by objects of class: [Date] TZ: UTC
xts Attributes:
NULL
但是，它“创建”了一个新的时间段。当我比较原始的dat与新的dat2时，我发现它现在从2020-01-05开始，而之前是从2020-01-06开始。
> head(dat)
AAPL.Close MSFT.Close GOOG.Close INTC.Close NVDA.Close
2020-01-06 299.80 159.03 1394.21 59.93 237.06
2020-01-07 298.39 157.58 1393.34 58.93 239.93
2020-01-08 303.19 160.09 1404.32 58.97 240.38
2020-01-09 309.63 162.09 1419.83 59.30 243.02
2020-01-10 310.33 161.34 1429.73 58.94 244.32
2020-01-13 316.96 163.28 1439.23 59.59 251.98
> head(dat2)
AAPL.Close MSFT.Close GOOG.Close INTC.Close NVDA.Close
2020-01-05 299.80 159.03 1394.21 59.93 237.06
2020-01-06 298.39 157.58 1393.34 58.93 239.93
2020-01-07 303.19 160.09 1404.32 58.97 240.38
2020-01-08 309.63 162.09 1419.83 59.30 243.02
2020-01-09 310.33 161.34 1429.73 58.94 244.32
2020-01-12 316.96 163.28 1439.23 59.59 251.98
如何纠正xts功能以获得正确的xts对象，并且不将数据移回一天。
数据：
dat <- structure(c(299.8, 298.39, 303.19, 309.63, 310.33, 316.96, 312.68,
311.34, 315.24, 318.73, 316.57, 317.7, 319.23, 318.31, 308.95,
317.69, 324.34, 323.87, 309.51, 308.66, 318.85, 321.45, 325.21,
320.03, 321.55, 319.61, 327.2, 324.87, 324.95, 319, 323.62, 320.3,
313.05, 298.18, 288.08, 292.65, 273.52, 273.36, 298.81, 289.32,
302.74, 292.92, 289.03, 266.17, 285.34, 275.43, 248.23, 277.97,
242.21, 252.86, 246.67, 244.78, 229.24, 224.37, 246.88, 245.52,
258.44, 247.74, 254.81, 254.29, 240.91, 244.93, 241.41, 159.03,
157.58, 160.09, 162.09, 161.34, 163.28, 162.13, 163.18, 166.17,
167.1, 166.5, 165.7, 166.72, 165.04, 162.28, 165.46, 168.04,
172.78, 170.23, 174.38, 180.12, 179.9, 183.63, 183.89, 188.7,
184.44, 184.71, 183.71, 185.35, 187.23, 187.28, 184.42, 178.59,
170.89, 168.07, 170.17, 158.18, 162.01, 172.79, 164.51, 170.55,
166.27, 161.57, 150.62, 160.92, 153.63, 139.06, 158.83, 135.42,
146.57, 140.4, 142.71, 137.35, 135.98, 148.34, 146.92, 156.11,
149.7, 160.23, 157.71, 152.11, 155.26, 153.83, 1394.21, 1393.34,
1404.32, 1419.83, 1429.73, 1439.23, 1430.88, 1439.2, 1451.7,
1480.39, 1484.4, 1485.95, 1486.65, 1466.71, 1433.9, 1452.56,
1458.63, 1455.84, 1434.23, 1485.94, 1447.07, 1448.23, 1476.23,
1479.23, 1508.68, 1508.79, 1518.27, 1514.66, 1520.74, 1519.67,
1526.69, 1518.15, 1485.11, 1421.59, 1388.45, 1393.18, 1318.09,
1339.33, 1389.11, 1341.39, 1386.52, 1319.04, 1298.41, 1215.56,
1280.39, 1215.41, 1114.91, 1219.73, 1084.33, 1119.8, 1096.8,
1115.29, 1072.32, 1056.62, 1134.46, 1102.49, 1161.75, 1110.71,
1146.82, 1162.81, 1105.62, 1120.84, 1097.88, 59.93, 58.93, 58.97,
59.3, 58.94, 59.59, 59.43, 58.94, 59.66, 59.6, 60.55, 62.73,
63.32, 68.47, 65.69, 67.31, 66.33, 66.47, 63.93, 64.42, 65.46,
67.34, 67.09, 66.02, 66.39, 67.41, 67.46, 67.44, 67.27, 66.14,
67.11, 65.45, 64.34, 61.76, 59.73, 59.65, 55.83, 55.52, 58.18,
55.97, 58.68, 56.96, 55.77, 50.85, 53.98, 51.66, 45.54, 54.43,
44.61, 50.08, 47.61, 45.94, 45.83, 49.58, 52.4, 51.26, 55.54,
52.37, 55.49, 54.12, 51.88, 54.35, 54.13, 237.06, 239.93, 240.38,
243.02, 244.32, 251.98, 247.28, 245.57, 248.93, 249.28, 247.94,
250.05, 252.86, 250.48, 240.2, 247.97, 245.54, 245.81, 236.43,
240.33, 247.13, 250.76, 254.245, 251.59, 262.97, 267.89, 272.54,
270.78, 289.79, 296.57, 314.7, 308.7, 294.07, 273.28, 262.05,
267.65, 252.6, 270.07, 276.43, 265.89, 284.51, 273.29, 266.04,
245.44, 261.08, 246.47, 216.31, 240.84, 196.4, 217.27, 202.82,
212.97, 205.75, 212.69, 249.18, 245.62, 257.24, 252.73, 265.59,
263.6, 243.07, 255.47, 243.91), .Dim = c(63L, 5L), .Dimnames = list(
NULL, c("AAPL.Close", "MSFT.Close", "GOOG.Close", "INTC.Close",
"NVDA.Close")), index = structure(c(1578265200, 1578351600,
1578438000, 1578524400, 1578610800, 1578870000, 1578956400, 1579042800,
1579129200, 1579215600, 1579561200, 1579647600, 1579734000, 1579820400,
1580079600, 1580166000, 1580252400, 1580338800, 1580425200, 1580684400,
1580770800, 1580857200, 1580943600, 1581030000, 1581289200, 1581375600,
1581462000, 1581548400, 1581634800, 1581980400, 1582066800, 1582153200,
1582239600, 1582498800, 1582585200, 1582671600, 1582758000, 1582844400,
1583103600, 1583190000, 1583276400, 1583362800, 1583449200, 1583708400,
1583794800, 1583881200, 1583967600, 1584054000, 1584313200, 1584399600,
1584486000, 1584572400, 1584658800, 1584918000, 1585004400, 1585090800,
1585177200, 1585263600, 1585519200, 1585605600, 1585692000, 1585778400,
1585864800), tzone = "", tclass = c("POSIXct", "POSIXt")), .indexTZ = "", class = c("xts",
"zoo"), .indexCLASS = c("POSIXct", "POSIXt"), tclass = c("POSIXct",
"POSIXt"), tzone = "", from = "20200105 18:04:21", to = "20200405 18:04:21", src = "IB", updated = structure(1586106262.20857, class = c("POSIXct",
"POSIXt")))
该几乎总是是由于从您的本地时区与UTC不同而从Date切换到POSIXct。 Date将成为UTC午夜，对于POSIXct，反映北美时区，则成为前一天。
R> as.Date("1970-01-01")
[1] "1970-01-01"
R> as.POSIXct(as.Date("1970-01-01"))
[1] "1969-12-31 18:00:00 CST"
R>
如果您每天的数据是（企业或市场），则可以将Date与xts结合使用，避免出现此问题。