Another question about time series from me.
I have a dataset which gives daily records of violent incidents in a psychiatric hospital over three years. With the help from my previous question I have been fiddling with it and am a bit happier about it now.
The thing I have now is that the daily series is very noisy. It fluctuates wildly, up and down, from 0 at times up to 20. Using loess plots and the forecast package (which I can highly recommend for novices like me) I just get a totally flat line, with massive confidence intervals from the forecast.
However, aggregating weekly or monthly the data make a lot more sense. They sweep down from the start of the series, and then increase again in the middle. Loess plotting and the forecast package both produce something that looks a lot more meaningful.
It does feel a bit like cheating though. Am I just preferring the aggregated versions because they look nice with no real validity to it?
Or would it be better to compute a moving average and use that as the basis? I'm afraid I don't understand the theory behind all this well enough to be confident about what is acceptable