CONTRIBUTE.md

Time-machines contributors guide

This project hopes to make it easier for humans, and also autonomous creatures, to get a rough idea of what time-series package or technique might be applicable to their domain. Popularity is not a good guide! If you wish to help with this search problem, there are easy and more involved ways to help.

• CONTRIBUTE COLAB NOTEBOOK if you like a package (pretty easy).
• CONTRIBUTE_BATCH_STYLE_MODELS to add new functionality using non-incremental methods.
• CONTRIBUTE_ONLINE_STYLE_MODELS to add new functionality using incremental methods.

It that seems daunting, read on.

New here?

You may ask yourself, "Well, how did I get here?" And you may ask yourself, "How do I work this?". And you may find yourself behind the wheel of a large automobile.

But enough 80's rock.

Specific goals

The strategy here:

1. Packaging a slew of fully autonomous univariate forecasting functions:

   • With a simple state-machine style signature ("skaters")
   • Drawing on whatever useful open-source Python packages can be found (and there's a lot of them)
   • Stacking, composing and otherwise exploiting existing skaters.
   • Computation of Elo ratings
   • ... so that Fast Python Timeseries Forecasting might become the norm.

2. Creating "crawlers" and other programs than operate in real-time and predict time-series at www.microprediction.org where:

   • They play this game, and in doing so,
   • Provide free high quality short-term prediction to anyone, anywhere, for any reason.
   • See also the TLDR

3. Demonstrating creative use of on-tap community prediction such as:

   • Multi-level autonomous crowd-sourcing where things feed back to the next step (see crypto examples).
   • Otherwise inventive use of general purpose prediction, conditional prediction, and prediction of ancilliary quantities to achieve intelligent systems in surprising ways. See my book.
   • Attacking otherwise thorny issues like defining anomaly detection in a way that isn't circular.
   • Driving investment returns for clients of Intech Investments, the project sponsor. After all if it helps with what might be the hardest problem of all (or at least the most competitive) it is a no-brainer that this will work elsewhere.

Contribution Patterns

I suppose it is nice if people follow, clap, share, heckle on medium, linked-in if that helps bring in contributors. Thanks. I suppose you can star, fork, watch timemachines.

Creating colab notebooks illustrating the use of Python timeseries packages

It helps speed the creation of autonomous algorithms, and Elo ratings, to have example notebooks for python time-series packages

0. See good first issues. Or search the same link for "Create colab notebook"

New package inclusion and approaches:

6. See CONTRIBUTE_BATCH_STYLE_MODELS to add new functionality using non-incremental methods.
7. See CONTRIBUTE_ONLINE_STYLE_MODELS to add new functionality using incremental methods.