Time series trend I have a time series which has a very strong upward trend for the first half, then very strong downward for the second half and finishes pretty much back where it started.
Should I split the data in two for analysis - or can I still account for a trend which is net neutral?
 A: Such time series are usually hard to model using ARIMA. But I suggest you plot the ACf and PACF of the series and see. It may require differencing. You can also test to see if the variance is constant; if it is not you transform. Once the data is stationary you can make sense out of it. 
As Nick has said splitting the series will generate two different processes and more information is needed. 
The series might appear to be oscillating: if that's the case the best thing to do is to try spectral analysis. 
A: I have had significant success in empirically identifying trend changes using AUTOBOX http://www.autobox.com/cms ,a commercially available piece of software that I have helped develop. You might want to look at stochastic vs deterministic trend/seasonality in time series forecasting for an interesting discussion.
A time trend model (deterministic in form) is as follows  y(t)=a+bx1+cx2
etc where x1=1,2,3,4....t and x2=0,0,0,0,0,1,2,3,4 thus one trend applies to observations 1−t and a second trend applies to observations 6 to t.
AUTOBOX's automatic empirical procedures have been very successful BUT like all "new science" or "advanced innovative procedures" it needs to be constantly aggressively challenged. Test all things but hold fast to what you know to be true ! Please post your data or send it to me privately and I will use the data and report the results to the group. If you are skittish about releasing the data simply code it by adding/subtracting a constant. The procedures used to identify the number of trends and the length of each trend is based partially on the work of Tsay http://www.unc.edu/~jbhill/tsay.pdf. All of these advanced procedures are not currently available in the free software market.
