Pachube and Timetric have something in common, but really I think our emphases are fairly different. Pachube’s very much focussed around routing sensor data, from what I’ve seen; we’re more general, in that we have an API you can use to hook up sensor data, but the thing we’re really focussed around, at least in my head – my colleagues might give you a different answer! – is comparison and analysis.

So, for example, using Timetric you can easily set up a calculation on a series or set of series – say a moving average or sum over the last seven points for weekly stats (click “filter” on a series page if you’re logged in to see what I mean). Equally, if you prefer, you can set up a new series which is the result of a formula acting on several other series: say you want to know the total time you’re spending on groups of your favourite sites by what kind of site they are (digg + reddit + Hacker News, or last.fm + youtube, etc…). Even better, you could take your procastination data, and mine, and build a new series equal to the difference between the two, and heckle me whenever I’m skiving – because whenever any of the underlying data’s updated, the series built on that data are updated too, so the results of formulae are always up to date. And so there’s a good range of data to compare with and build on, we’ve got a load of public data on there to go along with the data our users upload.

Here’s another example. Imagine there was another series – say the weather (which is public data we’re working on). Maybe you’re on the Web more when the weather’s cold? That’s something Timetric’d, hopefully, help you find out.

http://timetric.com/help/formulae/ is our primer on the modelling/calculation engine.

We’ve been doing some analysis of the data in the news, which is a pretty decent overview of the kind of analytics we’ve got – that’s at http://byline.timetric.com/. The most recent post about the G7 and BRIC economies uses derived/calculated series pretty heavily – click through to the underlying series from the graphs!

Spitting out a series of time-stamped events for Timetric to track should be trivial. Especially since it looks like the ‘startdate’ column in MeeTimer’s log table is simply the Unix Time stamp for that event (though expressed in milliseconds rather than seconds).

I’m thinking it would be easy to chart the session duration, or (with an accumulator) expressing it as a daily cumulative total. This would give a more meaningful ‘current value’ for a live chart e.g. ‘how long has roo been online today?’ (and eventually, ‘how long has roo been on site x today?’).

Our API docs are up at http://timetric.com/help/httpapi/, and there are Python bindings on Github. Drop me an email if I can help out – this’d be a really nifty tool to get in people’s hands!