The raw data is around 100mb of data.

I am fairly adept by now at bodging scripts together, and I'm using perl at the moment to analyse it and graphing the values. That's working very well.

When looking at an individual's data, the key question is prediction - where is the data going. Obviously it isn't accurate and it has a wide margin for error, but I'm not looking for a value. What I want to try to do is to categorise a string of patient data into

-static

-increasing

or decreasing.

I then want to colourcode the points on the graph according to trend

You don't have many data points- perhaps 6 or 7 spread out over a few years.

For now I'm using least squares, and using slope. But that isn't ideal and I assume would miss an U or n shaped pattern of results.

I appreciate that the accuracy will be questionable. But the key thing here is that since it will be compared to 5000+other data streams, what I want to try to do is identify which sets don't fit in with the pattern of the others.

So a slope value that is very different to another 5000 would be worth a closer look.

So that would then be coloured red.

So what better techniques than least squares would exist to do this.