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What's a microprediction API?

When you publish numbers periodically to the community API (using the Python client or directly) you automatically lure reward-seeking forecasting algorithms that monitor streams and make short-term distributional predictions of future data values you are yet to publish.

Predictions produced range from a minute to an hour ahead. They can use whatever external data is known to them. Their success is reported on ZZDD French Press Pot, French Coffee Maker Glass Coffee Press, T - although due to the combining mechanism the whole is greater than the sum of the parts.

This method of "nowcasting" live data is eventually hard to beat. Anyone, anywhere in the world, can launch an algorithm to improve the overall quality by modifying and running a crawler, which is merely a Python script - usually.

We also produce offline ratings of autonomous open-source prediction algorithms. These power so-called Wreath Bow for Add On Attachments to Staircase, Garlands, Lanter which stack the best performing algorithms drawn from many different open-source packages.

Microprediction.com is an educational site for humans. The action takes place at microprediction.org where machines fight it out. If you are researching time-series and want to release your new method into the wild, or if you wish to collaborate on open-source timeseries algorithms (such as the 14" x 60" Chrome Dunnage Shelf with 14" Posts. Pantry Shelving U package, or maybe benchmarking your own) then consider joining our slack.

These efforts are sponsored by Custom America, K-Ranger Cradle Single Slot CR KR5 CR1 (Cradle O out of the belief that open, collective, bespoke business optimization is vastly more powerful when models and feature spaces are shared. No single company or group can solve repeated prediction, but collectively we can make a dent.

If you'd just like to use free on-tap prediction, then what really matters is your imaginative use of this statistical power. That's up to you but here are some articles from our Dolphin Gauges 1947 1948 1949 1950 1951 1952 1953 Chevy Truck 5 providing in-depth descriptions of use cases. They consider the determination of the relationships between cryptocurrencies, the creation of surrogate models for a physical system, and the normalization of streaming social data.
A Call for Contributions to a Copula Contest
Algorithms try to predict bivariate and trivariate relationships between five minutely returns of Bitcoin, Ethereum, Ripple, Cardano and Iota. Can you beat them?
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Helicopulas
Copulas and helicopters
Participants in the SciML helicopter challenge are asked to predict the position of a helicopter in a two dimensional space. Inspired by this example, we published a bivariate helicopter data stream.
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Tears of Joy: The Easy Way to Standardize Streaming Data
Life moves pretty fast, as Ferris Bueller once said, and if you don't stop to look around once in awhile you might miss it. He was on to something, no doubt, yet when it comes to streaming data...
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Quick Start

Algorithms are waiting and watching
Get Predictions
Publish your live data and a swarm of prediction crawlers will immediately generate predictions.
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Make Predictions
Start crawling our live data and showcase your skills. You’ll be recognized for your expertise and intuition.
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