this is my first ever question on a website i use frequently!
This time series has given me much trouble over the last couple of days even after extensive googling, I suppose with TS theres no two series that look the same, hence my confusion!
I have the following ACF and PACF plots for my data:
After modelling NUMEROUS models in R (ARMA(p,q), with all combinations of p=0,1,2,3,4 and q=0,1,2,3,4) and looking at their theoretical acf plots, it seems R's auto.arima gave the best model with p=q=2.
But in my work, i don't want to just say i used R and inferred nothing from the ACF plots haha!
I understand that the slightly significant PACF at lag 1 indicates an AR component(?) and that as both plots 'slope off' it suggests a ARMA(p,q) model, but is there anything else im missing????
However ironically, no amount of differencing makes a difference, the plot still looks the same no matter what d equals. (R also tells me its not seasonal)
- What does the ACF plots tell me (main)
- Am i right in not differencing the data
Thanks a bunch!!! (feels good to finally posting a question - next stage is answering one lol)
(also a quick extra question for you keenos, when forecasting, what R package is best for visualising confidence intervals and simulations etc.)
The sum square of the residuals for the non seasonal model is 159799.8 And for the seasonal one 291955, suggesting the non-seasonal is better...?.?.?.