tablespoon
sktime
tablespoon | sktime | |
---|---|---|
9 | 8 | |
39 | 7,409 | |
- | 1.1% | |
5.3 | 9.8 | |
7 months ago | 4 days ago | |
Python | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
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tablespoon
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Statistical vs. Deep Learning forecasting methods
I use my package https://github.com/alexhallam/tablespoon to generate naive forecasts then evaluate the crps of the naive vs the crps of the alternative method. This “skill score” approach is very good.
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I made a library that makes naive forecasting easy
Source code is here tablespoon
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[P] Time-series Benchmark methods that are Simple and Probabilistic
tablespoon makes generating these naive methods easy while taking advantage of Stan's efficient No U-Turn Sampler - much the same way Facebook Prophet it built on top of Stan.
- [P] tablespoon: Time-series Benchmark methods that are Simple and Probabilistic
- [P]
- GitHub - alexhallam/tablespoon: 🥄✨Time-series Benchmark methods that are Simple and Probabilistic
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✨Announcing development of a benchmark forecasting library to be used as alongside AI forecasting methods. ✨
I just started the development of tablespoon. The purpose of this package is to make time-series benchmark forecasts that are simple and probabilistic.
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Time-series Benchmark methods that are Simple and Probabilistic
tablespoon
sktime
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Keras-tuner tuning hyperparam controlling feature size
I would recommend you to read the following paper: https://arxiv.org/abs/1909.04939 and their implementation: https://github.com/hfawaz/InceptionTime . Moreover, check out sktime: https://github.com/sktime/sktime
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Does anyone know a trusted Python package for applying Croston's Time series method?
I initially used the SkTime's Croston class SKTime Croston but when I try to get the fitted values using the steps in the discussion on github, the values are the same, a straight line throughout the in-sample to ou-of-sample predictions.
- Forecasting three months ahead.
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I Need Your Help: Convincing Reasons for Python over C# for ML Pipeline?
Time series -> https://github.com/alan-turing-institute/sktime have a look and have fun :)
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Good python time series libraries?
SKTime
- Scikit-Learn Version 1.0
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Sktime: Machine Learning for Time Series
https://github.com/alan-turing-institute/sktime
It provides specialized time series algorithms and scikit-learn compatible tools to build, tune and validate time series models for multiple learning problems.
sktime is built by an active open-source community, working together during regular meetings, workshops and sprints. For new contributors, we provide mentoring sessions and tutorials.
If you are interested in contributing or just a chat about the project, feel free to submit a PR or just reach out to us. We welcome all kinds of contributions: code, API design, testing, documentation, outreach, mentoring and more.
- Darts: Non-Facebook alternative for timeseries forecasting
What are some alternatives?
Bayeslite - BayesDB on SQLite. A Bayesian database table for querying the probable implications of data as easily as SQL databases query the data itself.
darts - A python library for user-friendly forecasting and anomaly detection on time series.
lambdo - Feature engineering and machine learning: together at last!
tslearn - The machine learning toolkit for time series analysis in Python
Numbers-Prophecy - An experiment to demonstrate the biases and predictability of our world.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Kats - Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.
uncertainty-baselines - High-quality implementations of standard and SOTA methods on a variety of tasks.
scikit-hts - Hierarchical Time Series Forecasting with a familiar API
scikit-learn - scikit-learn: machine learning in Python