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I have power data from a cycling power meter (this is only required for context, not for the mechanics of the problem) which looks somewhat stochastic. See attached image. power_image

This is a typical session which is based on a set of target 'levels' which are straight line targets of the power output required (so would look like a series of blocks with linear lines between them). The actual realised/achieved power output is naturally messy around this.

What I am after is extracting the target session from the messy power data (as I don't have access to this). It will essentially look like binary waves (straight level lines), though as you can see from the attached image, sometimes the power target is upward sloping.

A further complication is that for some of the other sessions the target effort blocks may be of short duration.

I have looked at various things to achieve this (wavelets etc...) but can't seem to find anything obvious...I am using R.

Any thoughts and help is greatly appreciated.

User123456789
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  • Do you only want to estimate the target levels for the periods where power is obviously flat (eg, ~9-13, ~25-30 min), or for every one of the periods between the apparent break points (ie, even those with upward/downward trends)? – Mark S Jan 12 '16 at 23:25
  • Hi Mark. Yes, I would like to estimate the full 60 mins...including up/downward trends. M – User123456789 Jan 13 '16 at 10:55
  • OK, it will be easier if you are planning to do "monitored" analyses wherein you would specify, a priori, the breakpoints. If unmonitored, it will be more difficult, but do-able. I suggest you check out the `R` packages `breakpoint`, `changepoint`, `structchange`, or `segmented`. – Mark S Jan 13 '16 at 14:41
  • Hi Mark, a priori sadly not an option. Thank you for your suggestions, I will take a look. M – User123456789 Jan 13 '16 at 14:50

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