R 计算时间序列上的滚动相关性
我有一个tbl df对象，我需要计算第二列和第三列中的值之间的22天滚动相关性。 这是我的对象的dput：R 计算时间序列上的滚动相关性,r,finance,rolling-computation,R,Finance,Rolling Computation,我有一个tbl df对象，我需要计算第二列和第三列中的值之间的22天滚动相关性。 这是我的对象的dput： structure(list(date = structure(c(1568937600, 1569196800, 1569283200, 1569369600, 1569456000, 1569542400, 1569801600, 1569888000, 1569974400, 1570060800, 1570147200, 1570406400, 1570492800, 1570
structure(list(date = structure(c(1568937600, 1569196800, 1569283200,
1569369600, 1569456000, 1569542400, 1569801600, 1569888000, 1569974400,
1570060800, 1570147200, 1570406400, 1570492800, 1570579200, 1570665600,
1570752000, 1571011200, 1571097600, 1571184000, 1571270400, 1571356800,
1571616000, 1571702400, 1571788800, 1571875200, 1571961600, 1572220800,
1572307200, 1572393600, 1572480000, 1572566400, 1572825600, 1572912000,
1572998400, 1573084800, 1573171200, 1573430400, 1573516800, 1573603200,
1573689600, 1573776000, 1574035200, 1574121600, 1574208000, 1574294400,
1574380800, 1574640000, 1574726400, 1574812800, 1574985600, 1575244800,
1575331200, 1575417600, 1575504000, 1575590400, 1575849600, 1575936000,
1576022400, 1576108800, 1576195200, 1576454400, 1576540800, 1576627200,
1576713600, 1576800000, 1577059200, 1577145600, 1577404800, 1577664000,
1577750400, 1577923200, 1578009600, 1578268800, 1578355200, 1578441600,
1578528000, 1578614400, 1578873600, 1578960000, 1579046400, 1579132800,
1579219200, 1579564800, 1579651200, 1579737600, 1579824000, 1580083200,
1580169600, 1580256000, 1580342400, 1580428800, 1580688000, 1580774400,
1580860800, 1580947200, 1581033600, 1581292800, 1581379200, 1581465600,
1581552000, 1581638400, 1581984000, 1582070400, 1582156800, 1582243200,
1582502400, 1582588800, 1582675200, 1582761600, 1582848000, 1583107200,
1583193600, 1583280000, 1583366400, 1583452800, 1583712000, 1583798400,
1583884800, 1583971200, 1584057600, 1584316800, 1584403200, 1584489600,
1584576000, 1584662400, 1584921600, 1585008000, 1585094400, 1585180800,
1585267200, 1585526400, 1585612800, 1585699200, 1585785600, 1585872000,
1586131200, 1586217600, 1586304000, 1586390400, 1586822400, 1586908800,
1586995200, 1587081600, 1587340800, 1587427200, 1587513600, 1587600000,
1587686400, 1587945600, 1588032000, 1588118400, 1588204800, 1588291200,
1588550400, 1588636800, 1588723200, 1588809600, 1589155200, 1589241600,
1589328000, 1589414400, 1589500800, 1589760000, 1589846400, 1589932800,
1590019200, 1590105600, 1590451200, 1590537600, 1590624000, 1590710400,
1590969600, 1591056000, 1591142400, 1591228800, 1591315200, 1591574400,
1591660800, 1591747200, 1591833600, 1591920000, 1592179200, 1592265600,
1592352000, 1592438400, 1592524800, 1592784000, 1592870400, 1592956800,
1593043200, 1593129600, 1593388800, 1593475200, 1593561600, 1593648000,
1593993600, 1594080000, 1594166400, 1594252800, 1594339200, 1594598400,
1594684800, 1594771200, 1594857600, 1594944000, 1595203200, 1595289600,
1595376000, 1595462400, 1595548800, 1595808000, 1595894400, 1595980800,
1596067200, 1596153600, 1596412800, 1596499200, 1596585600, 1596672000,
1596758400, 1597017600, 1597104000, 1597190400, 1597276800, 1597363200,
1597622400, 1597708800, 1597795200, 1597881600, 1597968000, 1598227200,
1598313600, 1598400000, 1598486400, 1598572800, 1598918400, 1599004800,
1599091200, 1599177600, 1599523200), tzone = "UTC", class = c("POSIXct",
"POSIXt")), pct_change = c(-0.000596715420165126, 0.00202340947964922,
-0.00730158486244312, -0.014938661539249, 0.00285144700422259,
-0.00339834001067637, -0.00209711565226938, -0.000307086514332147,
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0.0104573683004581, 0.0120692364486615, -0.00286134296490432,
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0.00114078921010915, -0.00109533561230724, 0.00421552918263898,
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0.0199592602820173, -0.00830487034909847, 0.0126320701923816,
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0.00126601561927261, 0.00614639173514924, -0.00670096962182707,
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0.0137674158859203, 0.00328687838817947, 0.000390438217303781,
-0.0203625863225501, -0.0110334664609242, -0.00574970859260905,
0.0035174314881728, 0.00932613613390663, -0.000497474821847449,
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0.0115418349860765, -0.000630859523089557, 0.00209068902203075,
-0.00176960918882073, -0.00263743166935199, -0.00100613855461638,
0.000325210859045598, 0.0066055774314958, 0.0155425710297051,
-0.000263772356322114, 0.000889232236059887, -0.00485134515050521,
0.00103805757000386, 0.00337171600827231, 0.00092670694368378,
-0.005054728949937, 0.010480854764867, -0.0215764218141165),
pct_change_second = c(-0.00932094208921797, 0.000818917212731368,
-0.0027521518829881, 0.00519655387979917, -0.0116533058303007,
-0.0140137463826531, 0.00855392091246724, -0.00613817538393172,
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-0.00513149858369002, -0.00541441675047788, 0.0157920224491813,
0.0150073048366694, 0.0106632863775459, 0.00604407808585764,
-0.00443203712949445, -0.007262266547329, -0.000539774051100301,
-0.0182597134535798, 0.00731547899432845, 0.000263028779812938,
0.00251633712311902, -0.00475031035429309, 0.0150377339562151,
0.00612428547948301, -0.0132957765501841, -0.001984519247229,
-0.00649254127908213, 0.0208650232574573, 0.00951444197766564,
0.0129432128498977, -0.00636795207105839, 0.0165556167836716,
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0.00610837832340035, 0.00404328562725829, -0.0113988567458328,
0.00724692697647314, 0.0215818195181752, -0.0005478992874822,
0.0087238804996817, -0.0169716528074893, 0.00518213964825958,
-0.0291933261773754)), row.names = c(NA, -240L), class = c("tbl_df",
"tbl", "data.frame"), na.action = structure(1:22, .Names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18", "19", "20", "21", "22"), class = "omit"))
price_data_corr <- rollapplyr(price_data_join, 22, function(price_data_join) cor(price_data_join[, 2], price_data_join[, 3]), by.column = FALSE)
我建议采取下一个办法。看起来您可以在
mutate（）rollapply（）cbind（）图书馆（动物园）
图书馆（dplyr）
#代码
mycor尝试使用滚动函数包。下面的代码计算22天的相关性（能够处理不规则的时间序列）。
您可以滚动整个data.frame
，这意味着函数中的x
将在k=“22天”
窗口内成为数据的子集。窗口是在引用数据中的列的idx=“date”
上计算的（如果您愿意，也可以直接指定idx=data$date
）
有关更多信息，请访问
库（运行程序）
跑步者(
x=数据，
f=函数（x）{
cor（x$pct变化，x$pct变化秒）
},
k=“22天”，
idx=“日期”
)
总目（数据）
#更改日期更改更改第二次更改
#2019-09-20-0.0005967154-0.0093209421北美
# 2 2019-09-23 0.0020234095 0.0008189172 1.0000000
# 3 2019-09-24 -0.0073015849 -0.0027521519 0.1066286
# 4 2019-09-25 -0.0149386615 0.0051965539 -0.5860520
# 5 2019-09-26 0.0028514470 -0.0116533058 -0.7029877
# 6 2019-09-27 -0.0033983400 -0.0140137464 -0.5868171
享受猜测，可能是因为您使用的是TIBLE，当data.frame为TIBLE时，一列上的子集将返回一个包含1列的数据帧，即list。尝试将您的数据更改为dataframe:price\u data\u join@G.Grothendieck噢，这是一个打字错误！我现在就搬。非常感谢您的澄清。@G.Grothendieck我将删除该信息，并将该信息归功于您！
Error in cor(price_data_join[, 2], price_data_join[, 3]) :
'x' must be numeric
library(zoo)
library(dplyr)
#Code
mycor <- function(x) cor(x[, 1], x[, 2])
#Apply
DF2 <- price_data_join %>%
mutate(Cor = rollapplyr(cbind(pct_change, pct_change_second), 22, mycor, by.column = FALSE, fill = NA))
# A tibble: 240 x 4
date pct_change pct_change_second Cor
<dttm> <dbl> <dbl> <dbl>
1 2019-09-20 00:00:00 -0.000597 -0.00932 NA
2 2019-09-23 00:00:00 0.00202 0.000819 NA
3 2019-09-24 00:00:00 -0.00730 -0.00275 NA
4 2019-09-25 00:00:00 -0.0149 0.00520 NA
5 2019-09-26 00:00:00 0.00285 -0.0117 NA
6 2019-09-27 00:00:00 -0.00340 -0.0140 NA
7 2019-09-30 00:00:00 -0.00210 0.00855 NA
8 2019-10-01 00:00:00 -0.000307 -0.00614 NA
9 2019-10-02 00:00:00 -0.0109 -0.00546 NA
10 2019-10-03 00:00:00 0.00342 0.00305 NA
# ... with 230 more rows