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From: Hugo Duncan (hugoduncan_at_[hidden])
Date: 2007-07-26 14:03:58


> It shouldn't be the
> series' job to keep a circular buffer of data for the algorithm to use.
> Rather, if the algorithms requires a buffer of previously seen data, it
> should cache the data itself,

Might it be worth a comment to the above affect in the documentation? It
seems to be a fundamental principal for the library, which, at least to
me, wasn't clear.

> as in the rolling average implementation I
> sent around a few days ago.

I missed that until after I had posted.

> The Sequence concept on which all the time series' are built requires
> readable and incrementable cursors. That means the time series
> algorithms *should* all work with an "input" or single-pass series types
> -- that is, one with a destructive read. That would be the way to go
> IMO. I could see a time series type implemented in terms of std::istream
> that reads runs from std::cin, for instance. Or more practially, one
> that memory-maps parts of a huge file and traverses it with single pass
> cursors. This would be a very interesting time series! The algorithms
> haven't been tested with such a single pass series, but I don't see a
> fundamental problem with it.

Excellent. Files normally contain multivariate data though, so presumabley
it would require multiple series backed by a common object to do the
memory mapping?

> 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 point).
Even the sparse series seems to require a constant discretisation.

> Yup, no convolution yet. Sure would be nice. Patches welcome! :-)



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