Making a time series analysis package in python - advice or assistance sought

[beliavsky at aol.com]

Ray Tomes wrote:
> Hi Folks
>
> I am an old codger who has much experience with computers
> in the distant past before all this object oriented stuff.
> Also I have loads of software in such languages as FORTRAN
> and BASIC, QBASIC etc that is very useful except that it
> really doesn't like to run on modern operating systems and
> has hopeless graphics resolution and lack of ease of use in
> some ways.

The Fortran code, which I assume is Fortran 77 or earlier, should run
fine on "modern operating systems" using free (g77, g95, or gfortran)
or commercial compilers.

> My desire is to get all the facilities available in my
> old programs working in a modern platform with flexible
> and high-res graphics and easy to use. Ideally I might
> find some good coders that are interested in the subject
> who would assist me, alternatively some help in getting
> started because there is so much info and so many resources
> and libraries etc that I don't know where to start.
>
> My package will have the following capabilities:
> 1. Able to read time series data in a variety of formats.
> 2. Able to create, manipulate and save time series files.
> 3. Able to do vector arithmetic on time series, including
> dozens of functions.

Fortran 90 and later versions have array operations, as does NumPy. You
could convert parts of the FORTRAN code to F90

> 4. Loop and macro facilities to simplify repetitive stuff.
> 5. Flexible high-resolution graphic presentation.
> 6. Built in functions to include:
> FFT / fourier analysis, MESA / maximum entropy spectral analysis,
> multiple regression, canonical correlation etc etc etc.
> I have code for all these mostly in FORTRAN, some QBASIC.
>
> The applications of the package include:
> 1. Analysis of time series data from many branches of science.
> 2. Economic / business models including forecasting.
> 3. Markets, stocks, commodities forecasting.
> 4. Interdisciplinary causal analysis.
> 5. Many more

There exist public domain codes for many of the topics you mention, and
I think several are part of NumPy. Many statistical algorithms are in
R, for which the underlying C and Fortran code is available. I suggest
that you identify which of your algorithms are not publicly available
and focus on those, making an R package of them. I am interested in
MESA. Then you can exploit the R graphics and language (called S) and
have your work easily accessible to many users.