On Mon, May 25, 2009 at 8:30 PM, Pierre GM

On May 25, 2009, at 8:06 PM, josef.pktd@gmail.com wrote:

The problem is, if the functions are enhanced in the current numpy, then scikits.timeseries is not (yet) available.

Mmh, I'm not following you here...

The original question was how we can enhance numpy.financial, eg. np.irr So we are restricted to use only what is available in numpy and in standard python.

Ah OK. But it seems that you're now running into a pb w/ dates handling, which might be a bit too specialized for numpy. Anyway, the call isn't mine.

I looked at your moving functions, autocorrelation function and so on a while ago. That's were I learned how to use np.correlate or the scipy versions of it, and the filter functions. I've written the standard array versions for the moving functions and acf, ccf, in one of my experiments.

The moving functions were written in C and they work even w/ timeseries (they work quite OK w/ pure MaskedArraysP. We put them in scikits.timeseries because it was easier to have them there than in scipy, for example.

If Skipper has enough time in his google summer of code, we would like to include some basic timeseries econometrics (ARMA, VAR, ...?) however most likely only for regularly spaced data.

Well, we can easily restrict the functions to the case were there's no missing data nor missing dates. Checking the mask is easy, and we have a method to chek the dates (is_valid)

Anyhow, if the pb you have are just to specify dates, I really think you should give the scikits a try. And send feedback, of course...

Skipper intends to write some examples to show how to work with the extensions to scipy.stats, which, I think, will include examples using time series, besides recarrays, and other array types.

Dealing with TimeSeries is pretty much the same thing as dealing with MaskedArray, with the extra convenience of converting from one frequency to another and so forth.... Quite often, an analysis can be performed by dropping the .dates part, working on the .series part (the underlying MaskedArray), and repatching the dates at the end...

Is there a time line for including the timeseries scikits in numpy/ scipy? With code that is intended for incorporation in numpy/scipy, we are restricted in our external dependencies.

I can't tell, because the decision is not mine. For what I understood, there could be an inclusion in scipy if there's a need for it. For that, we need more users end more feedback.... If you catch my drift...

Thanks for the info, we will keep this in mind. Personally, I still think of data just as an array or matrix of numbers, when they still have dates and units attached to them, they are usually a pain. And I'm only slowly getting used to the possibility that it doesn't necessarily need to be so painful. (I didn't know you moved the moving functions to C, I thought I saw them in python.) Josef