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|
%global _empty_manifest_terminate_build 0
Name: python-microprediction
Version: 1.1.34
Release: 1
Summary: Client for www.microprediction.org turnkey community prediction
License: MIT
URL: https://github.com/microprediction/microprediction
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/8c/d3/9e470193a782e1864c28a4c312bbc118d946366f2987e01907599fd5e562/microprediction-1.1.34.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-pandas
Requires: python3-contexttimer
Requires: python3-requests
Requires: python3-getjson
Requires: python3-microconventions
Requires: python3-pytz
Requires: python3-pycoingecko
Requires: python3-tdigest
Requires: python3-genson
Requires: python3-hyperopt
Requires: python3-scikit-learn
Requires: python3-statsmodels
Requires: python3-copulas
%description
# microprediction [tldr](https://microprediction.github.io/microprediction/tldr), [docs](https://microprediction.github.io/microprediction/), [client](https://github.com/microprediction/microprediction) and [live leaderboards](https://www.microprediction.org/leaderboard.html) 
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](https://microprediction.github.io/microprediction/glossary)) markets are surprisingly accurate too, and you can create one any time you like.
See [tldr](https://microprediction.github.io/microprediction/tldr) or just [instantly participate](https://microprediction.github.io/microprediction/setup) and you'll grok it, I promise.
### Provocations (more in the [book](https://mitpress.mit.edu/books/microprediction))
- A market beat 97% of participants in the M6 contest, and the rest were lucky ([post](https://www.linkedin.com/posts/petercotton_the-options-market-beat-94-of-participants-activity-7020917422085795840-Pox0?utm_source=share&utm_medium=member_desktop)) and [announcement](https://www.linkedin.com/posts/spyros-makridakis-b2ba5a52_congratulations-to-the-global-winners-of-activity-7028775981133791232-Mlzs?utm_source=share&utm_medium=member_desktop).
- No timeseries model should ever be called SOTA again. [Discuss](https://www.linkedin.com/posts/petercotton_timeseries-forecasting-timeseriesanalysis-activity-6987561356862353408-iy2Z?utm_source=share&utm_medium=member_desktop) and disagree but your argument [probably reduces to a contradiction](https://github.com/microprediction/building_an_open_ai_network/discussions/19).
- Modeling [is central planning](https://www.linkedin.com/posts/petercotton_feedback-activity-7029452936686489600-8iuX?utm_source=share&utm_medium=member_desktop). Why limit capabilities to a single mind, algorithm or company? ([discuss](https://www.linkedin.com/posts/petercotton_machinelearning-reinforcementlearning-datascience-activity-6992560556863803392-FOM6?utm_source=share&utm_medium=member_desktop))
- Somebody's algorithm or data will find signal in your model residuals, someday ([instructions](https://microprediction.github.io/microprediction/residuals)).
- Most of "AI" will be done analogously, eventually, though this will take work. See the [book](https://mitpress.mit.edu/books/microprediction) or [challenge me](https://github.com/microprediction/building_an_open_ai_network/discussions).
## Try it out ([docs](https://microprediction.github.io/microprediction/), [install](https://github.com/microprediction/microprediction/blob/master/INSTALL.md) and live [help](https://microprediction.github.io/microprediction/meet.html))
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](https://microprediction.github.io/microprediction/) and, therein, observe that you can receive live [help](https://microprediction.github.io/microprediction/meet.html) getting started on Fridays, or in the [slack channel](https://microprediction.github.io/microprediction/slack.html). Key points:
- No barriers to entry. To predict, just open this [notebook](https://github.com/microprediction/microprediction/blob/master/notebook_examples_submission/enter_microprediction_contest.ipynb) and run it, or cut and paste a [one line bash command](https://microprediction.github.io/microprediction/setup).
- The microprediction platform makes it [pretty trivial](https://microprediction.github.io/microprediction/publish.html) to initiate your own bespoke market too. Just ask Thomas Hjelde Thorensen who recently [posted](https://www.linkedin.com/posts/thomashthoresen_datascience-microprediction-timeseriesforecasting-activity-6999971006274514944-lDID?utm_source=share&utm_medium=member_desktop) about his experience.
- [Many algorithms](https://www.microprediction.org/leaderboard.html) already competing to predict [other streams](https://www.microprediction.org/browse_streams.html) can easily predict yours too.
- Many more will do so in the future. Anyone can [launch a new algorithm](https://microprediction.github.io/microprediction/predict.html) using anything they like in the Julia, R or Python [ecosystem](https://www.microprediction.com/blog/popular-timeseries-packages) 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](https://microprediction.github.io/microprediction/slack.html) say). You can also just grab data, see the [reader](https://github.com/microprediction/microprediction/blob/master/microprediction/reader.py).
# The [TimeMachines](https://github.com/microprediction/timemachines), [Precise](https://github.com/microprediction/precise), and [HumpDay](https://github.com/microprediction/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](https://github.com/microprediction/timemachines) | [Timeseries Elo ratings](https://microprediction.github.io/timeseries-elo-ratings/html_leaderboards/univariate-k_003.html) | Most popular packages ([list](https://github.com/microprediction/timemachines/tree/main/timemachines/skaters)) | [microprediction streams](https://www.microprediction.org/browse_streams.html) |
| Global derivative-free optimization | [humpday](https://github.com/microprediction/humpday) | [Optimizer Elo ratings](https://microprediction.github.io/optimizer-elo-ratings/html_leaderboards/overall.html) | Most popular packages ([list](https://github.com/microprediction/humpday/tree/main/humpday/optimizers)) | A mix of classic and new [objectives](https://github.com/microprediction/humpday/tree/main/humpday/objectives) |
| Covariance, precision, correlation | [precise](https://github.com/microprediction/precise) | See [notebooks](https://github.com/microprediction/precise/tree/main/examples_colab_notebooks) | [cov](https://github.com/microprediction/precise/blob/main/LISTING_OF_COV_SKATERS.md) and [portfolio](https://github.com/microprediction/precise/blob/main/LISTING_OF_MANAGERS.md) 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](https://www.microprediction.org/browse_streams.html) "house" algorithms use these packages
Advances in time-series prediction funnel down into microprediction algorithms in various ways:
1. The "[/skaters](https://github.com/microprediction/timemachines/tree/main/timemachines/skaters)" provide canonical, single-line of code access to functionality drawn from packages like [river](https://github.com/online-ml/river), [pydlm](https://github.com/wwrechard/pydlm), [tbats](https://github.com/intive-DataScience/tbats), [pmdarima](http://alkaline-ml.com/pmdarima/), [statsmodels.tsa](https://www.statsmodels.org/stable/tsa.html), [neuralprophet](https://neuralprophet.com/), Facebook [Prophet](https://facebook.github.io/prophet/),
Uber's [orbit](https://eng.uber.com/orbit/), Facebook's [greykite](https://engineering.linkedin.com/blog/2021/greykite--a-flexible--intuitive--and-fast-forecasting-library) and more.
2. The [StreamSkater](https://microprediction.github.io/microprediction/predict-using-python-streamskater.html) makes it easy to use any "skater".
3. Choices are sometimes advised by [Elo ratings](https://microprediction.github.io/timeseries-elo-ratings/html_leaderboards/special-k_003.html), but anyone can do what they want.
4. It's not too hard to use my [HumpDay](https://github.com/microprediction/humpday) package for offline meta-param tweaking, et cetera.
5. It's not too hard to use my [precise](https://github.com/microprediction/precise) package for online ensembling.
There are other ways. Look for CODE badges on [leaderboards](https://www.microprediction.org/leaderboard.html).
### Some microprediction platform repos
- The [muid](https://github.com/microprediction/muid) identifier package is explained in this [video](https://vimeo.com/397352413).
- [microconventions](https://github.com/microprediction/microconventions) captures things common to client and server, and may answer many of your more specific questions about prediction horizons, et cetera.
- [rediz](https://github.com/microprediction/rediz) contains server side code. For the brave.
- There are other rats and mice like [getjson](https://github.com/microprediction/getjson), [runthis](https://github.com/microprediction/runthis) and [momentum](https://github.com/microprediction/momentum).
# Some of my other packages:
- [winning](https://github.com/microprediction/winning) - A recently published fast algorithm for inferring relative ability from win probability.
- [embarrassingly](https://github.com/microprediction/embarrassingly) - A speculative approach to robust optimization that sends impure objective functions to optimizers.
- [pandemic](https://github.com/microprediction/pandemic) - Ornstein-Uhlenbeck epidemic simulation (related [paper](https://arxiv.org/abs/2005.10311))
- [firstdown](https://github.com/microprediction/firstdown) - The repo that aspires to ruin the great game of football. See Wilmott [paper](https://github.com/microprediction/firstdown/blob/main/wilmott_paper/44-49_Cotton_PDF5_Jan22%20(2).pdf).
- [m6](https://github.com/microprediction/m6) - Illustrates fast numerical rank probability calculations, using [winning](https://github.com/microprediction/winning). However since the rules changed, this isn't that useful for M6 anymore. The [precise](https://github.com/microprediction/precise) package is way more useful, and put one person on the podium!
# About me ([home](https://github.com/microprediction/home))
- [blog](https://microprediction.medium.com)
- [slack channel](https://microprediction.github.io/microprediction/slack.html)
- [office hours](https://microprediction.github.io/microprediction/meet.html)
- [papers, articles etc](https://github.com/microprediction/home)
%package -n python3-microprediction
Summary: Client for www.microprediction.org turnkey community prediction
Provides: python-microprediction
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-microprediction
# microprediction [tldr](https://microprediction.github.io/microprediction/tldr), [docs](https://microprediction.github.io/microprediction/), [client](https://github.com/microprediction/microprediction) and [live leaderboards](https://www.microprediction.org/leaderboard.html) 
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](https://microprediction.github.io/microprediction/glossary)) markets are surprisingly accurate too, and you can create one any time you like.
See [tldr](https://microprediction.github.io/microprediction/tldr) or just [instantly participate](https://microprediction.github.io/microprediction/setup) and you'll grok it, I promise.
### Provocations (more in the [book](https://mitpress.mit.edu/books/microprediction))
- A market beat 97% of participants in the M6 contest, and the rest were lucky ([post](https://www.linkedin.com/posts/petercotton_the-options-market-beat-94-of-participants-activity-7020917422085795840-Pox0?utm_source=share&utm_medium=member_desktop)) and [announcement](https://www.linkedin.com/posts/spyros-makridakis-b2ba5a52_congratulations-to-the-global-winners-of-activity-7028775981133791232-Mlzs?utm_source=share&utm_medium=member_desktop).
- No timeseries model should ever be called SOTA again. [Discuss](https://www.linkedin.com/posts/petercotton_timeseries-forecasting-timeseriesanalysis-activity-6987561356862353408-iy2Z?utm_source=share&utm_medium=member_desktop) and disagree but your argument [probably reduces to a contradiction](https://github.com/microprediction/building_an_open_ai_network/discussions/19).
- Modeling [is central planning](https://www.linkedin.com/posts/petercotton_feedback-activity-7029452936686489600-8iuX?utm_source=share&utm_medium=member_desktop). Why limit capabilities to a single mind, algorithm or company? ([discuss](https://www.linkedin.com/posts/petercotton_machinelearning-reinforcementlearning-datascience-activity-6992560556863803392-FOM6?utm_source=share&utm_medium=member_desktop))
- Somebody's algorithm or data will find signal in your model residuals, someday ([instructions](https://microprediction.github.io/microprediction/residuals)).
- Most of "AI" will be done analogously, eventually, though this will take work. See the [book](https://mitpress.mit.edu/books/microprediction) or [challenge me](https://github.com/microprediction/building_an_open_ai_network/discussions).
## Try it out ([docs](https://microprediction.github.io/microprediction/), [install](https://github.com/microprediction/microprediction/blob/master/INSTALL.md) and live [help](https://microprediction.github.io/microprediction/meet.html))
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](https://microprediction.github.io/microprediction/) and, therein, observe that you can receive live [help](https://microprediction.github.io/microprediction/meet.html) getting started on Fridays, or in the [slack channel](https://microprediction.github.io/microprediction/slack.html). Key points:
- No barriers to entry. To predict, just open this [notebook](https://github.com/microprediction/microprediction/blob/master/notebook_examples_submission/enter_microprediction_contest.ipynb) and run it, or cut and paste a [one line bash command](https://microprediction.github.io/microprediction/setup).
- The microprediction platform makes it [pretty trivial](https://microprediction.github.io/microprediction/publish.html) to initiate your own bespoke market too. Just ask Thomas Hjelde Thorensen who recently [posted](https://www.linkedin.com/posts/thomashthoresen_datascience-microprediction-timeseriesforecasting-activity-6999971006274514944-lDID?utm_source=share&utm_medium=member_desktop) about his experience.
- [Many algorithms](https://www.microprediction.org/leaderboard.html) already competing to predict [other streams](https://www.microprediction.org/browse_streams.html) can easily predict yours too.
- Many more will do so in the future. Anyone can [launch a new algorithm](https://microprediction.github.io/microprediction/predict.html) using anything they like in the Julia, R or Python [ecosystem](https://www.microprediction.com/blog/popular-timeseries-packages) 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](https://microprediction.github.io/microprediction/slack.html) say). You can also just grab data, see the [reader](https://github.com/microprediction/microprediction/blob/master/microprediction/reader.py).
# The [TimeMachines](https://github.com/microprediction/timemachines), [Precise](https://github.com/microprediction/precise), and [HumpDay](https://github.com/microprediction/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](https://github.com/microprediction/timemachines) | [Timeseries Elo ratings](https://microprediction.github.io/timeseries-elo-ratings/html_leaderboards/univariate-k_003.html) | Most popular packages ([list](https://github.com/microprediction/timemachines/tree/main/timemachines/skaters)) | [microprediction streams](https://www.microprediction.org/browse_streams.html) |
| Global derivative-free optimization | [humpday](https://github.com/microprediction/humpday) | [Optimizer Elo ratings](https://microprediction.github.io/optimizer-elo-ratings/html_leaderboards/overall.html) | Most popular packages ([list](https://github.com/microprediction/humpday/tree/main/humpday/optimizers)) | A mix of classic and new [objectives](https://github.com/microprediction/humpday/tree/main/humpday/objectives) |
| Covariance, precision, correlation | [precise](https://github.com/microprediction/precise) | See [notebooks](https://github.com/microprediction/precise/tree/main/examples_colab_notebooks) | [cov](https://github.com/microprediction/precise/blob/main/LISTING_OF_COV_SKATERS.md) and [portfolio](https://github.com/microprediction/precise/blob/main/LISTING_OF_MANAGERS.md) 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](https://www.microprediction.org/browse_streams.html) "house" algorithms use these packages
Advances in time-series prediction funnel down into microprediction algorithms in various ways:
1. The "[/skaters](https://github.com/microprediction/timemachines/tree/main/timemachines/skaters)" provide canonical, single-line of code access to functionality drawn from packages like [river](https://github.com/online-ml/river), [pydlm](https://github.com/wwrechard/pydlm), [tbats](https://github.com/intive-DataScience/tbats), [pmdarima](http://alkaline-ml.com/pmdarima/), [statsmodels.tsa](https://www.statsmodels.org/stable/tsa.html), [neuralprophet](https://neuralprophet.com/), Facebook [Prophet](https://facebook.github.io/prophet/),
Uber's [orbit](https://eng.uber.com/orbit/), Facebook's [greykite](https://engineering.linkedin.com/blog/2021/greykite--a-flexible--intuitive--and-fast-forecasting-library) and more.
2. The [StreamSkater](https://microprediction.github.io/microprediction/predict-using-python-streamskater.html) makes it easy to use any "skater".
3. Choices are sometimes advised by [Elo ratings](https://microprediction.github.io/timeseries-elo-ratings/html_leaderboards/special-k_003.html), but anyone can do what they want.
4. It's not too hard to use my [HumpDay](https://github.com/microprediction/humpday) package for offline meta-param tweaking, et cetera.
5. It's not too hard to use my [precise](https://github.com/microprediction/precise) package for online ensembling.
There are other ways. Look for CODE badges on [leaderboards](https://www.microprediction.org/leaderboard.html).
### Some microprediction platform repos
- The [muid](https://github.com/microprediction/muid) identifier package is explained in this [video](https://vimeo.com/397352413).
- [microconventions](https://github.com/microprediction/microconventions) captures things common to client and server, and may answer many of your more specific questions about prediction horizons, et cetera.
- [rediz](https://github.com/microprediction/rediz) contains server side code. For the brave.
- There are other rats and mice like [getjson](https://github.com/microprediction/getjson), [runthis](https://github.com/microprediction/runthis) and [momentum](https://github.com/microprediction/momentum).
# Some of my other packages:
- [winning](https://github.com/microprediction/winning) - A recently published fast algorithm for inferring relative ability from win probability.
- [embarrassingly](https://github.com/microprediction/embarrassingly) - A speculative approach to robust optimization that sends impure objective functions to optimizers.
- [pandemic](https://github.com/microprediction/pandemic) - Ornstein-Uhlenbeck epidemic simulation (related [paper](https://arxiv.org/abs/2005.10311))
- [firstdown](https://github.com/microprediction/firstdown) - The repo that aspires to ruin the great game of football. See Wilmott [paper](https://github.com/microprediction/firstdown/blob/main/wilmott_paper/44-49_Cotton_PDF5_Jan22%20(2).pdf).
- [m6](https://github.com/microprediction/m6) - Illustrates fast numerical rank probability calculations, using [winning](https://github.com/microprediction/winning). However since the rules changed, this isn't that useful for M6 anymore. The [precise](https://github.com/microprediction/precise) package is way more useful, and put one person on the podium!
# About me ([home](https://github.com/microprediction/home))
- [blog](https://microprediction.medium.com)
- [slack channel](https://microprediction.github.io/microprediction/slack.html)
- [office hours](https://microprediction.github.io/microprediction/meet.html)
- [papers, articles etc](https://github.com/microprediction/home)
%package help
Summary: Development documents and examples for microprediction
Provides: python3-microprediction-doc
%description help
# microprediction [tldr](https://microprediction.github.io/microprediction/tldr), [docs](https://microprediction.github.io/microprediction/), [client](https://github.com/microprediction/microprediction) and [live leaderboards](https://www.microprediction.org/leaderboard.html) 
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](https://microprediction.github.io/microprediction/glossary)) markets are surprisingly accurate too, and you can create one any time you like.
See [tldr](https://microprediction.github.io/microprediction/tldr) or just [instantly participate](https://microprediction.github.io/microprediction/setup) and you'll grok it, I promise.
### Provocations (more in the [book](https://mitpress.mit.edu/books/microprediction))
- A market beat 97% of participants in the M6 contest, and the rest were lucky ([post](https://www.linkedin.com/posts/petercotton_the-options-market-beat-94-of-participants-activity-7020917422085795840-Pox0?utm_source=share&utm_medium=member_desktop)) and [announcement](https://www.linkedin.com/posts/spyros-makridakis-b2ba5a52_congratulations-to-the-global-winners-of-activity-7028775981133791232-Mlzs?utm_source=share&utm_medium=member_desktop).
- No timeseries model should ever be called SOTA again. [Discuss](https://www.linkedin.com/posts/petercotton_timeseries-forecasting-timeseriesanalysis-activity-6987561356862353408-iy2Z?utm_source=share&utm_medium=member_desktop) and disagree but your argument [probably reduces to a contradiction](https://github.com/microprediction/building_an_open_ai_network/discussions/19).
- Modeling [is central planning](https://www.linkedin.com/posts/petercotton_feedback-activity-7029452936686489600-8iuX?utm_source=share&utm_medium=member_desktop). Why limit capabilities to a single mind, algorithm or company? ([discuss](https://www.linkedin.com/posts/petercotton_machinelearning-reinforcementlearning-datascience-activity-6992560556863803392-FOM6?utm_source=share&utm_medium=member_desktop))
- Somebody's algorithm or data will find signal in your model residuals, someday ([instructions](https://microprediction.github.io/microprediction/residuals)).
- Most of "AI" will be done analogously, eventually, though this will take work. See the [book](https://mitpress.mit.edu/books/microprediction) or [challenge me](https://github.com/microprediction/building_an_open_ai_network/discussions).
## Try it out ([docs](https://microprediction.github.io/microprediction/), [install](https://github.com/microprediction/microprediction/blob/master/INSTALL.md) and live [help](https://microprediction.github.io/microprediction/meet.html))
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](https://microprediction.github.io/microprediction/) and, therein, observe that you can receive live [help](https://microprediction.github.io/microprediction/meet.html) getting started on Fridays, or in the [slack channel](https://microprediction.github.io/microprediction/slack.html). Key points:
- No barriers to entry. To predict, just open this [notebook](https://github.com/microprediction/microprediction/blob/master/notebook_examples_submission/enter_microprediction_contest.ipynb) and run it, or cut and paste a [one line bash command](https://microprediction.github.io/microprediction/setup).
- The microprediction platform makes it [pretty trivial](https://microprediction.github.io/microprediction/publish.html) to initiate your own bespoke market too. Just ask Thomas Hjelde Thorensen who recently [posted](https://www.linkedin.com/posts/thomashthoresen_datascience-microprediction-timeseriesforecasting-activity-6999971006274514944-lDID?utm_source=share&utm_medium=member_desktop) about his experience.
- [Many algorithms](https://www.microprediction.org/leaderboard.html) already competing to predict [other streams](https://www.microprediction.org/browse_streams.html) can easily predict yours too.
- Many more will do so in the future. Anyone can [launch a new algorithm](https://microprediction.github.io/microprediction/predict.html) using anything they like in the Julia, R or Python [ecosystem](https://www.microprediction.com/blog/popular-timeseries-packages) 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](https://microprediction.github.io/microprediction/slack.html) say). You can also just grab data, see the [reader](https://github.com/microprediction/microprediction/blob/master/microprediction/reader.py).
# The [TimeMachines](https://github.com/microprediction/timemachines), [Precise](https://github.com/microprediction/precise), and [HumpDay](https://github.com/microprediction/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](https://github.com/microprediction/timemachines) | [Timeseries Elo ratings](https://microprediction.github.io/timeseries-elo-ratings/html_leaderboards/univariate-k_003.html) | Most popular packages ([list](https://github.com/microprediction/timemachines/tree/main/timemachines/skaters)) | [microprediction streams](https://www.microprediction.org/browse_streams.html) |
| Global derivative-free optimization | [humpday](https://github.com/microprediction/humpday) | [Optimizer Elo ratings](https://microprediction.github.io/optimizer-elo-ratings/html_leaderboards/overall.html) | Most popular packages ([list](https://github.com/microprediction/humpday/tree/main/humpday/optimizers)) | A mix of classic and new [objectives](https://github.com/microprediction/humpday/tree/main/humpday/objectives) |
| Covariance, precision, correlation | [precise](https://github.com/microprediction/precise) | See [notebooks](https://github.com/microprediction/precise/tree/main/examples_colab_notebooks) | [cov](https://github.com/microprediction/precise/blob/main/LISTING_OF_COV_SKATERS.md) and [portfolio](https://github.com/microprediction/precise/blob/main/LISTING_OF_MANAGERS.md) 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](https://www.microprediction.org/browse_streams.html) "house" algorithms use these packages
Advances in time-series prediction funnel down into microprediction algorithms in various ways:
1. The "[/skaters](https://github.com/microprediction/timemachines/tree/main/timemachines/skaters)" provide canonical, single-line of code access to functionality drawn from packages like [river](https://github.com/online-ml/river), [pydlm](https://github.com/wwrechard/pydlm), [tbats](https://github.com/intive-DataScience/tbats), [pmdarima](http://alkaline-ml.com/pmdarima/), [statsmodels.tsa](https://www.statsmodels.org/stable/tsa.html), [neuralprophet](https://neuralprophet.com/), Facebook [Prophet](https://facebook.github.io/prophet/),
Uber's [orbit](https://eng.uber.com/orbit/), Facebook's [greykite](https://engineering.linkedin.com/blog/2021/greykite--a-flexible--intuitive--and-fast-forecasting-library) and more.
2. The [StreamSkater](https://microprediction.github.io/microprediction/predict-using-python-streamskater.html) makes it easy to use any "skater".
3. Choices are sometimes advised by [Elo ratings](https://microprediction.github.io/timeseries-elo-ratings/html_leaderboards/special-k_003.html), but anyone can do what they want.
4. It's not too hard to use my [HumpDay](https://github.com/microprediction/humpday) package for offline meta-param tweaking, et cetera.
5. It's not too hard to use my [precise](https://github.com/microprediction/precise) package for online ensembling.
There are other ways. Look for CODE badges on [leaderboards](https://www.microprediction.org/leaderboard.html).
### Some microprediction platform repos
- The [muid](https://github.com/microprediction/muid) identifier package is explained in this [video](https://vimeo.com/397352413).
- [microconventions](https://github.com/microprediction/microconventions) captures things common to client and server, and may answer many of your more specific questions about prediction horizons, et cetera.
- [rediz](https://github.com/microprediction/rediz) contains server side code. For the brave.
- There are other rats and mice like [getjson](https://github.com/microprediction/getjson), [runthis](https://github.com/microprediction/runthis) and [momentum](https://github.com/microprediction/momentum).
# Some of my other packages:
- [winning](https://github.com/microprediction/winning) - A recently published fast algorithm for inferring relative ability from win probability.
- [embarrassingly](https://github.com/microprediction/embarrassingly) - A speculative approach to robust optimization that sends impure objective functions to optimizers.
- [pandemic](https://github.com/microprediction/pandemic) - Ornstein-Uhlenbeck epidemic simulation (related [paper](https://arxiv.org/abs/2005.10311))
- [firstdown](https://github.com/microprediction/firstdown) - The repo that aspires to ruin the great game of football. See Wilmott [paper](https://github.com/microprediction/firstdown/blob/main/wilmott_paper/44-49_Cotton_PDF5_Jan22%20(2).pdf).
- [m6](https://github.com/microprediction/m6) - Illustrates fast numerical rank probability calculations, using [winning](https://github.com/microprediction/winning). However since the rules changed, this isn't that useful for M6 anymore. The [precise](https://github.com/microprediction/precise) package is way more useful, and put one person on the podium!
# About me ([home](https://github.com/microprediction/home))
- [blog](https://microprediction.medium.com)
- [slack channel](https://microprediction.github.io/microprediction/slack.html)
- [office hours](https://microprediction.github.io/microprediction/meet.html)
- [papers, articles etc](https://github.com/microprediction/home)
%prep
%autosetup -n microprediction-1.1.34
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-microprediction -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.34-1
- Package Spec generated
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