Cleaning up time series

Here's is chart depicting a time series. Imagine the number of hits on a webpage, the volume traded on the stock exchange for a given stock, or a metric of the kind.

My immediate goal is to isolate trend data for further analysis. In order to do this properly, I must take into account the "shock" (say, a hit storm) that occurs at periods 24-28. I can then interpolate or simply ignore the interval.

The funny thing is that I am not aware of tools or techniques that would do this automatically. I am not an expert in the field, but not uneducated either. (familiar with most procedures found in TSP).

The chart to which I refer above is the simplest example I found in a sample of about 10 I just examined. A little more involved case is found here. In this second example, there is evidence that the series moves from a first state (A), stable and low intensity, to a second state (B) trending upwards, perturbed by two impulses (starting at period 49 and 60), where an impulse differs from a shock in that a shock adds a constant to the trend, whereas the impulse shifts data (say, hits that would have occurred on period 54 are moved forward to period 50, as if a promotional event had displaced demand).

If anyone out there is aware of "smart" procedures that would hilight events in order to extract trend data, I would appreciate pointers. (just in case -- a regression will not do, because the resulting trend data would have spread disturbances on a prediction line, and the predicted data would become useless anyway precisely because I want to run a special form of non linear model on these inputs).

If anyone is interested is one way or another, please let me know as well. I'll put relevant posts under the "project: mapping time series" category.

(an example is detailed here. Click here for an index of all posts on this thread)