在 R 中，auto.arima 无法捕获季节性

Question

auto.arima() 没有为我的系列提供季节性成分，尽管我可以看到有一件礼物。该函数为我提供了一个非季节性的 ARIMA 阶模型 (5,0,0)。所以，当我尝试使用该模型进行预测时，它只是给出了平均值。时间序列为澳大利亚墨尔本十年来每日最低气温。

Click this link to see the data and the auto.arima forecast

`

library(readr)

temp <- read_csv("~/Downloads/Melbourne Minimum Temp.csv", 
                 col_types = cols(Date = col_date(format = "%m/%d/%y"), 
                                  Temp = col_number()))

t <- ts(temp$Temp, start = temp$Date\[1], end = temp$Date[nrow(temp)])

auto.arima(t, trace = T)

`

尝试将数据用作 ts 对象、xts 对象和向量。

Answer 1

只是报告一个很好的解释 - 像往常一样 - Rob Hyndman 的博文。

https://robjhyndman.com/hyndsight/dailydata/

你问题的相关部分说（完全引用页面）：

When the time series is long enough to take in more than a year, then it may be necessary to allow for annual seasonality as well as weekly seasonality. In that case, a multiple seasonal model such as TBATS is required.

y <- msts(x, seasonal.periods=c(7,365.25))
fit <- tbats(y)
fc <- forecast(fit)
plot(fc)

This should capture the weekly pattern as well as the longer annual pattern. The period 365.25 is the average length of a year allowing for leap years. In some countries, alternative or additional year lengths may be necessary.

我认为它完全符合您的要求。

我也试过简单地用 msts 创建时间序列

y <- msts(x[1:1800], seasonal.periods=c(7,365.25))

（为了更快，我把时间序列减半了）

然后运行 auto.arima() 直接在上面，用 D=1

强制季节性成分

fc = auto.arima(y,D=1,trace=T,stepwise = F)

这需要一段时间.. 因为我设置了 stepwise = FALSE（如果你想让它查看所有没有快捷方式的组合，你也可以设置 approximation=FALSE）

Series: y 
ARIMA(1,0,3)(0,1,0)[365] 

Coefficients:
         ar1      ma1      ma2      ma3
      0.9036  -0.3647  -0.3278  -0.0733
s.e.  0.0500   0.0571   0.0405   0.0310

sigma^2 estimated as 12.63:  log likelihood=-3854.1
AIC=7718.19   AICc=7718.23   BIC=7744.54

然后是预测

for_fc = forecast(fc)
plot(for_fc)

我在输出之上添加了一个带有完整时间序列（红色）的图形情节（for_fc）它似乎工作正常 - 但它只是一个快速测试。

在 R 中，auto.arima 无法捕获季节性

In R, auto.arima fails to capture seasonality

r

time-series

forecasting