根據月份或季度拆分時間序列數據以比較均值和方差值

Question

這是解決上述問題的擴展問題。

我有一個來自 Yahoo Finance 的數據集，其中涵蓋了大約兩年的 Apple 每日股票數據。 現在我想根據月份或季度拆分數據集，以便比較均值和方差值（檢查平穩性）

這是數據集：

      Date       Adj.Close

 1   2018-01-02    41.38024
 2   2018-01-03    41.37303
 3   2018-01-04    41.56522
 4   2018-01-05    42.03845
 5   2018-01-08    41.88231
 6   2018-01-09    41.87751
 7   2018-01-10    41.86789
 8   2018-01-11    42.10571
 9   2018-01-12    42.54050
 10  2018-01-16    42.32431
 11  2018-01-17    43.02335
 12  2018-01-18    43.06179
 13  2018-01-19    42.86961
 14  2018-01-22    42.51889
 15  2018-01-23    42.52850
 16  2018-01-24    41.85107
 17  2018-01-25    41.10399
 18  2018-01-26    41.20008
 19  2018-01-29    40.34730
 20  2018-01-30    40.10948
 21  2018-01-31    40.21999
 22  2018-02-01    40.30407
 23  2018-02-02    38.55526
 24  2018-02-05    37.59198

我該怎么做？ 謝謝！

Answer 1

我們可以使用zoo中的as.yearmon/as.yearqtr對它們進行分組並獲取匯總統計信息

library(dplyr)
library(zoo)
df %>%
   group_by(yearmonth = as.yearmon(Date)) %>% 
   summarise(startDate = min(Date), 
             avg = mean(Adj.Close), sd = sd(Adj.Close), .groups = 'drop') %>%
   select(-yearmonth)

或'yearqtr'

df %>%
   group_by(yearqtr = as.yearqtr(Date)) %>% 
   summarise(startDate = min(Date), 
             avg = mean(Adj.Close), sd = sd(Adj.Close), .groups = 'drop') %>%
   select(-yearqtr)
# A tibble: 1 x 3
#  startDate    avg    sd
#  <date>     <dbl> <dbl>
#1 2018-01-02  41.4  1.35

數據

df <- structure(list(Date = structure(c(17533, 17534, 17535, 17536, 
17539, 17540, 17541, 17542, 17543, 17547, 17548, 17549, 17550, 
17553, 17554, 17555, 17556, 17557, 17560, 17561, 17562, 17563, 
17564, 17567), class = "Date"), Adj.Close = c(41.38024, 41.37303, 
41.56522, 42.03845, 41.88231, 41.87751, 41.86789, 42.10571, 42.5405, 
42.32431, 43.02335, 43.06179, 42.86961, 42.51889, 42.5285, 41.85107, 
41.10399, 41.20008, 40.3473, 40.10948, 40.21999, 40.30407, 38.55526, 
37.59198)), row.names = c("1", "2", "3", "4", "5", "6", "7", 
"8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", 
"19", "20", "21", "22", "23", "24"), class = "data.frame")