microprediction 1.2.0

Client for www.microprediction.org turnkey community prediction

microprediction tldr, docs, client and live leaderboards

Packages and a platform for effecting autonomous prediction using lightweight markets instead of models because:

• Markets are better at prediction than models - just harder to create and wield, until now.
• Small "microprediction" (glossary) markets are surprisingly accurate too, and even chatGPT can create one.

See tldr or just instantly participate and you'll grok it, I promise.

Provocations (more in the book)

• A market beat 97% of participants in the M6 contest(post) and announcement.
• No timeseries model should ever be called SOTA again. Discuss and disagree but your argument probably reduces to a contradiction.
• Modeling is central planning, a fatal conceit. Why limit capabilities to a single mind, algorithm or company?
• Somebody's algorithm or data will find signal in your model residuals, someday (instructions).
• Most of "AI" will be done analogously, eventually, though this will take work. See the book or challenge me.

Try it out (docs, install and live help)

If you would like to see how easy it is to wield a new kind of market to effect turnkey distributional prediction, see the docs and, therein, observe that you can receive live help getting started on Fridays, or in the slack channel. Key points:

• No barriers to entry. To predict, just open this notebook and run it, or cut and paste a one line bash command.
• The microprediction platform makes it pretty trivial to initiate your own bespoke market too. Just ask Thomas Hjelde Thorensen who recently posted about his experience.
• Many algorithms already competing to predict other streams can easily predict yours too.
• Many more will do so in the future. Anyone can launch a new algorithm using anything they like in the Julia, R or Python ecosystem for example (it's a data interface).

Too hard? If you have a CSV with historical data (one column per variable) you can just send it to me (chat in slack say). You can also just grab data, see the reader.

The TimeMachines, Precise, and HumpDay packages

I also maintain three benchmarking packages to help me, and maybe you, surf the open-source wave.

Topic	Package	Elo ratings	Methods	Data sources
Univariate time-series	timemachines	Timeseries Elo ratings	Most popular packages (list)	microprediction streams
Global derivative-free optimization	humpday	Optimizer Elo ratings	Most popular packages (list)	A mix of classic and new objectives
Covariance, precision, correlation	precise	See notebooks	cov and portfolio lists	Stocks, electricity etc

These packages aspire to advance online autonomous prediction in a small way, but also help me notice if anyone else does.

How microprediction.org "house" algorithms use these packages

Advances in time-series prediction funnel down into microprediction algorithms in various ways:

1. The "/skaters" provide canonical, single-line of code access to functionality drawn from packages like river, pydlm, tbats, pmdarima, statsmodels.tsa, neuralprophet, Facebook Prophet, Uber's orbit, Facebook's greykite and more.

2. The StreamSkater makes it easy to use any "skater".

3. Choices are sometimes advised by Elo ratings, but anyone can do what they want.

4. It's not too hard to use my HumpDay package for offline meta-param tweaking, et cetera.

5. It's not too hard to use my precise package for online ensembling.

There are other ways. Look for CODE badges on leaderboards.

Some microprediction platform repos

• The muid identifier package is explained in this video.
• microconventions captures things common to client and server, and may answer many of your more specific questions about prediction horizons, et cetera.
• rediz contains server side code. For the brave.
• There are other rats and mice like getjson, runthis and momentum.

Some of my other packages:

• winning - A recently published fast algorithm for inferring relative ability from win probability.
• embarrassingly - A speculative approach to robust optimization that sends impure objective functions to optimizers.
• pandemic - Ornstein-Uhlenbeck epidemic simulation (related paper)
• firstdown - The repo that aspires to ruin the great game of football. See Wilmott paper.
• m6 - Illustrates fast numerical rank probability calculations, using winning. However since the rules changed, this isn't that useful for M6 anymore. The precise package is way more useful, and put one person on the podium!

About me (home)

• blog
• slack channel
• office hours
• papers, articles etc

Acknowledgements

The live microprediction platform is supported by Intech Investments and not, as rumoured, the Center for Artificial Artificial Intelligence.