https://github.com/microprediction/successor

Predicts the next number in a sequence, or next k
https://github.com/microprediction/successor

Last synced: 4 months ago
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Predicts the next number in a sequence, or next k

• Host: GitHub
• URL: https://github.com/microprediction/successor
• Owner: microprediction
• License: mit
• Created: 2021-10-12T23:55:31.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-10-26T16:01:11.000Z (over 3 years ago)
• Last Synced: 2024-12-17T03:12:54.375Z (4 months ago)
• Language: Python
• Size: 59.6 KB
• Stars: 1
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# successor ![tests](https://github.com/microprediction/successor/workflows/tests/badge.svg) ![tests-38](https://github.com/microprediction/successor/workflows/tests-38/badge.svg) ![tests-37](https://github.com/microprediction/successor/workflows/tests-37/badge.svg) ![pypi](https://github.com/microprediction/successor/workflows/deploy-pypi/badge.svg) ![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)

Uses pre-trained tensorflow models to predict the next k entries in a sequence 

## Install

    pip install successor

You may get better performance by first installing tensorflow following the [instructions](https://www.tensorflow.org/install) and perhaps

reading this [thread](https://stackoverflow.com/questions/66092421/how-to-rebuild-tensorflow-with-the-compiler-flags). 

## Use 

See [basic_use](https://github.com/microprediction/successor/tree/main/examples/basic_use.py)

    

    # 1. Import a skater

    from successor.skaters.scalarskaters.scalartsaskaters import successor_tsa_aggressive_d0_ensemble as f

    # 2. Univariate data

    import numpy as np

    y = list(np.cumsum(np.random.randn(1000)))

    # 3. Initialize state to empty dict

    s = {}

    # 4. Give it some data (observations) one at a time, each time passing it back the state s

    for yi in y:

        x, x_std, s = f(y=yi,s=s,k=1)

Skaters follow the convention established by the [timemachines](https://github.com/microprediction/timemachines) library and you are encouraged to read the description of

the "skater" signature if anything is confusing. 

## Side effect

Dumps tensorflow model files to disk. 

## Benchmarking

See [Elo ratings](https://microprediction.github.io/timeseries-elo-ratings/html_leaderboards/univariate-k_001.html)