我们有以下数据值和时间序列标记：
Lines <- "date,time,data
20/03/2014,07:10,9996792524
21/04/2014,07:10,8479115468
21/09/2014,07:10,11394750532
16/10/2014,07:10,9594869828
18/11/2014,07:10,10850291677
08/12/2014,07:10,10475635302
22/01/2015,07:10,10116010939
26/02/2015,07:10,11206949341
20/03/2015,07:10,11975140317
09/04/2015,07:10,11526960332
29/04/2015,07:10,9986194500
16/09/2015,07:10,11501088256
13/10/2015,07:10,11833183163
10/11/2015,07:10,13246940910
16/12/2015,07:10,13255698568
27/01/2016,07:10,13775653990
23/02/2016,07:10,13567323648
22/03/2016,07:10,14607415705
11/04/2016,07:10,13835444224
04/04/2016,07:10,14118970743"
我们使用 zoo 库在R中读取了这些内容，如下所示：
z <- read.zoo(text = Lines, sep = ",", header = TRUE,
index = 1:2, tz = "", format = "%d/%m/%Y %H:%M")
plot(z)
现在我们正在尝试在此时间序列数据上使用自动ARIMA模型 . 但是，我们收到错误：
amar_fit <- auto.arima(z)
#Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
# 0 (non-NA) cases
我们做错了什么？我们如何在这个不规则的时空系列中成功地拟合ARIMA（或其他）预测模型？