I have two groups of time-series, each group represents one type of data. However within each group, each time series may be fitted with a different ARIMA(p,d,q) from the other time series in the same group.
I need to create a single model for each group (
Model_group2). I tried the approach mentioned by Rob Hyndman in:
Estimating same model over multiple time series.
I need to use these two models to classify any time series to one of these two groups. For each time series, I calculated the AIC of
Model_group2, and the model with smaller AIC will mean that the time series belongs to its corresponding group.
I have three problems:
I received a warning message
Series: ts ARIMA(3,0,2) with non-zero mean Coefficients: ar1 ar2 ar3 ma1 ma2 intercept 0.0714 0.1417 0.0000 0.0893 -0.0871 0.1169 s.e. NaN 0.1381 0.0127 NaN 0.1436 0.0026 sigma^2 estimated as 0.2202: log likelihood=-33822.63 AIC=67659.26 AICc=67659.26 BIC=67725.99 Warning message: In sqrt(diag(x$var.coef)) : NaNs produced
This message was returned by only one of the group models. Does that mean that the fitted model is not correct?
I got two different results using
auto.arima(ts, allowdrift=FALSE, stepwise=FALSE) auto.arima(ts, allowdrift=FALSE, stepwise=TRUE)
When I tested the resulting models, the majority of the time-series were classified as
group_1, even when I test one of the time series used to build the long time series of
group_2. I need to mention here that the composed time series of
group_1is quite shorter than the time series of
group_2. Are there any expected reasons for that?