我从excel
导入这个数据框test <- readXL("C:/Users/MOUSTAPHA/Desktop/test.xlsx", rownames=TRUE,
header=TRUE, na="", sheet="Feuil1", stringsAsFactors=TRUE)
test
jan. feb. mar ap may juin. july aug. sep. oct nov. dec.
2010 62.1 89.0 86.1 59.0 18.8 92.1 15.8 92.2 20.3 22.6 20.8 44.0
2011 20.3 22.6 35.2 14.4 80.5 20.9 15.5 17.2 43.3 20.5 76.7 17.0
2012 28.8 21.4 28.4 31.6 36.3 44.8 53.2 52.6 55.6 0.0 49.4 93.3
2013 11.6 0.0 10.9 40.2 17.3 24.6 29.5 95.1 45.7 23.8 24.1 24.7
2014 10.9 16.2 58.9 14.3 20.7 15.5 14.6 14.4 14.2 44.1 17.7 26.2
2015 53.4 25.1 15.0 155.0 9.1 45.6 32.5 15.0 0.0 15.2 34.3 30.8
我尝试使用数据创建（testts）时间序列：
testts <- ts(test , start = c(2010,1), frequency=12)
testts
jan feb mar ap may juin july aug sep oct nov dec
2010 62 89 86 59 19 92 16 92 20 23 21 44
2011 20 23 35 14 81 21 16 17 43 21 77 17
2012 29 21 28 32 36 45 53 53 56 0 49 93
2013 12 0 11 40 17 25 30 95 46 24 24 25
2014 11 16 59 14 21 16 15 14 14 44 18 26
2015 53 25 15 155 9 46 33 15 0 15 34 31
当我尝试使用decompose(testts)对其进行分解时出现此错误：
过滤器＆＃39;比时间序列长
如何导入数据并将其分解，就像示例＆＃39; AirPassengers＆＃39; ？
答案 0 :(得分：0)
将data.frame传递给ts时似乎存在问题。但是，在将unlist添加到您正确的代码中时，可以轻松解决此问题，即
testts <- ts(unlist(test) , start = c(2010, 1), frequency = 12)
decompose(testts)
这是与分解的时间序列相关的控制台输出。
$x
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2010 62.1 20.3 28.8 11.6 10.9 53.4 89.0 22.6 21.4 0.0 16.2 25.1
2011 86.1 35.2 28.4 10.9 58.9 15.0 59.0 14.4 31.6 40.2 14.3 155.0
2012 18.8 80.5 36.3 17.3 20.7 9.1 92.1 20.9 44.8 24.6 15.5 45.6
2013 15.8 15.5 53.2 29.5 14.6 32.5 92.2 17.2 52.6 95.1 14.4 15.0
2014 20.3 43.3 55.6 45.7 14.2 0.0 22.6 20.5 0.0 23.8 44.1 15.2
2015 20.8 76.7 49.4 24.1 17.7 34.3 44.0 17.0 93.3 24.7 26.2 30.8
$seasonal
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2010 -2.724722 15.576944 9.364444 -10.520556 -11.089722 -18.260556 36.462778 -15.523056 -5.204722 1.179444 -14.821389 15.561111
2011 -2.724722 15.576944 9.364444 -10.520556 -11.089722 -18.260556 36.462778 -15.523056 -5.204722 1.179444 -14.821389 15.561111
2012 -2.724722 15.576944 9.364444 -10.520556 -11.089722 -18.260556 36.462778 -15.523056 -5.204722 1.179444 -14.821389 15.561111
2013 -2.724722 15.576944 9.364444 -10.520556 -11.089722 -18.260556 36.462778 -15.523056 -5.204722 1.179444 -14.821389 15.561111
2014 -2.724722 15.576944 9.364444 -10.520556 -11.089722 -18.260556 36.462778 -15.523056 -5.204722 1.179444 -14.821389 15.561111
2015 -2.724722 15.576944 9.364444 -10.520556 -11.089722 -18.260556 36.462778 -15.523056 -5.204722 1.179444 -14.821389 15.561111
$trend
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2010 NA NA NA NA NA NA 31.11667 32.73750 33.34167 33.29583 35.26667 35.66667
2011 32.81667 31.22500 31.30833 33.40833 35.00417 40.33750 42.94583 42.02917 44.24583 44.84167 43.51667 41.67917
2012 42.81250 44.46250 45.28333 45.18333 44.58333 40.07500 35.39167 32.55833 30.55417 31.76667 32.02083 32.74167
2013 33.72083 33.57083 33.74167 37.00417 39.89583 38.57500 37.48750 38.83333 40.09167 40.86667 41.52500 40.15417
2014 35.90000 33.13750 31.08333 25.92083 24.18750 25.43333 25.46250 26.87500 28.00833 26.85000 26.09583 27.67083
2015 29.99167 30.73750 34.47917 38.40417 37.69583 37.60000 NA NA NA NA NA NA
$random
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2010 NA NA NA NA NA NA 21.420556 5.385556 -6.736944 -34.475278 -4.245278 -26.127778
2011 56.008056 -11.601944 -12.272778 -11.987778 34.985556 -7.076944 -20.408611 -12.106111 -7.441111 -5.821111 -14.395278 97.759722
2012 -21.287778 20.460556 -18.347778 -17.362778 -12.793611 -12.714444 20.245556 3.864722 19.450556 -8.346111 -1.699444 -2.702778
2013 -15.196111 -33.647778 10.093889 3.016389 -14.206111 12.185556 18.249722 -6.110278 17.713056 53.053889 -12.303611 -40.715278
2014 -12.875278 -5.414444 15.152222 30.299722 1.102222 -7.172778 -39.325278 9.148056 -22.803611 -4.229444 32.825556 -28.031944
2015 -6.466944 30.385556 5.556389 -3.783611 -8.906111 14.960556 NA NA NA NA NA NA
$figure
[1] -2.724722 15.576944 9.364444 -10.520556 -11.089722 -18.260556 36.462778 -15.523056 -5.204722 1.179444 -14.821389 15.561111
$type
[1] "additive"
attr(,"class")
[1] "decomposed.ts"