问题
I am trying to fit arima model using auto.arima function in R. The result is showing order (0,0,0) even though the data is non-stationary.
auto.arima(x,approximation=TRUE)
ARIMA(0,0,0) with non-zero mean
Can someone advice why such results are coming? Btw i am running this function on only 10 data points.
回答1:
10 data points is a very low number of observations for estimating an ARIMA model. I doubt that you can make any sensible estimation based on this. Moreover, the estimated model may depend strongly on the part of a time series you looked at and adding only very few observations can change the characteristics of the estimated model significantly. For example:
When I take a time series with only 10 observations, I also get a ARIMA(0,0,0) model:
library(forecast)
vec1 <- ts(c(10.26063, 10.60462, 10.37365, 11.03608, 11.19136, 11.13591, 10.84063, 10.66458, 11.06324, 10.75535), frequency = 12)
fit1 <- auto.arima(vec1)
summary(fit1)
However, if I use about 30 observations, it an ARIMA(1,0,0) model is estimated:
vec2 <- ts(c(10.260626, 10.604616, 10.373652, 11.036079, 11.191359, 11.135914, 10.840628, 10.664575, 11.063239, 10.755350,
10.158032, 10.653669, 10.659231, 10.483478, 10.739133, 10.400146, 10.205993, 10.827950, 11.018257, 11.633930,
11.287756, 11.202727, 11.244572, 11.452180, 11.199706, 10.970823, 10.386131, 10.184201, 10.209338, 9.544736), frequency = 12)
fit1 <- auto.arima(vec2)
summary(fit1)
If I use the whole time series (413 observations), the auto.arima function estimates a "ARIMA(2,1,4)(0,0,1)[12] with drift".
Thus, I would think that 10 observation is indeed not enough information for fitting a model.
来源：https://stackoverflow.com/questions/31349400/auto-arima-is-not-showing-any-order