timemachines

Some uses

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Simple uses of this package:

1. Use some of the functionality of a subset of the popular python time-series packages like river, pydlm, tbats, pmdarima, statsmodels.tsa, neuralprophet, Facebook Prophet, Uber’s orbit, Facebook’s greykite and more with one line of code. Or use home-spun methods like thinking_fast_and_slow that you’ll only find here.

1.5 Augment popular models, say by using one line of code to make them more regular as per this article.

1. Peruse Elo ratings or use them programatically. There’s also a recommendation colab notebook you can open and run. And you might consider the use of forever functions that get better over time without your doing anything.

More advanced uses of this package:

1. Make your own autonomous algorithms and watch them compete. See the daily $125 prize and open this notebook to understand the rudimentary mechanics of submitting distributions. Any skaters in this package can be turned into a “crawler” pretty easily, as demonstrated in the stream skater examples.
2. Use stacking to create better skaters. One can, for example, draw on a large inventory of online portfolio managers in the precise package to assign positive weights to a collection of skaters based on their model residuals. If you have the computation time, you can draw on all the latest advances in portfolio theory.
3. Use hyper-parameter tuning and turn “almost” autonomous algorithms, or combinations of the same, into fully autonomous algorithms using just about any global optimizer you can think of via the humpday package. At present there are about one hundred derivative-free methods including choices from Ax-Platform, bayesian-optimization, DLib, HyperOpt, NeverGrad, Optuna, Platypus, PyMoo, PySOT, Scipy classic and shgo, Skopt, nlopt, Py-Bobyaq, and UltraOpt.
4. Use composition to chase residuals (like boosting). Determine whether skaters here help predict your proprietary in-house model residuals.
5. Write your next paper and easily benchmark your work, using live data. Or write an Empirical Article That Isn’t Immediately Stale.

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