From: Hugo Duncan (hugoduncan_at_[hidden])
Date: 2007-07-26 22:12:39
>>> I'm not 100% sure I understand your use case. But most of the series
>>> types and algorithms allow non-discrete sequences. That is, the offsets
>>> can be floating point. Could that help?
>> Yes I had seen that, but wasn't sure how it worked for sampled data. In
>> my case I have a multiple time series with a (common) sample time that
>> varies stochastically between 40-60ms. It wasn't clear to me that the
>> offsets could be non-constant stride (whether integer or floating
>> Even the sparse series seems to require a constant discretisation.
> So, you have a discrete series (i.e., values at offsets), but the
> offsets map to discretizations according to some piecewise function? Did
> I get that right? That's interesting.
That would be interesting indeed, but no. These are logged data that are
being collected from a bus as they arrive. The arrival time period is not
> Most of the time series algorithms don't actually use the discretization
> for anything besides type-checking. The only exception is integrate(),
> which multiplies runs by the discretization. How do you use the
mostly to resample to fit the data in with other time series that have a
> Perhaps your usage can be accommodated with an extra
> function that maps from cursor to discretization. I think this is a
> refinement to the TimeSeries concept, but one which most of the
> algorithms (besides integrate) won't care about.
Thanks for taking the time to consider these use cases.
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