As is evident from my last post on ARIMA I'm searching for various approaches to Time series forecasting,, It seems that ARIMA won't do what I'm looking for.
I have a pretty good method that is yielding good results (maybe by chance but the results are pretty good).
This is also the data that I asked about in my prior question on getting ARIMA to work.. I now get that the better and currently unavailable method should be SARIMA, but I suspect that even that would not be as accurate. I'm not desperate to use these methods but rather am using the fact that I understand this data domain pretty well and therefore it is useful to try using different methods to learn them..
Anyhow I'm trying to write up what I've learned and where I'm still confused (more the latter) Which will at least serve to link resources I have located on the web.
This is the notebook Chad Fulton redid so nicely on my former ARIMA question, modified with Data linkages and all imports
http://nbviewer.ipython.org/7604008 As one can see the forecast in the last cell does not reflect the seasonality nor the upward drift from the ecconomic cycle. Having fiddled with the ARIMA params for some time I don't seem to be able to correct that.
So Ive rambled a bit, but the question is if any of you have advice or guidance, ideas or code, I'd like to believe that carrying this out more could be a useful example for highly seasonal time series in general and hopefully the python tools to carry it out with? The papers and docs are usually so dnes with the math it is hard for the non-stats majors such as myself to apply, so well worked examples are very valuable (and rare!)