tablespoon
ES-RNN-Pytorch
| tablespoon | ES-RNN-Pytorch | |
|---|---|---|
| 9 | 1 | |
| 39 | 11 | |
| - | - | |
| 5.3 | 10.0 | |
| 7 months ago | about 5 years ago | |
| Python | Jupyter Notebook | |
| MIT 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
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- 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
ES-RNN-Pytorch
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Statistical vs. Deep Learning forecasting methods
Hmmm. A bit strange there is already a M4 competition where a deep learning model won. I know because I reimplemented it as a toy version in python here: https://github.com/leanderloew/ES-RNN-Pytorch
It was actually very cool because the model was a melt of exponential smoothing and dl.
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.
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
lambdo - Feature engineering and machine learning: together at last!
Numbers-Prophecy - An experiment to demonstrate the biases and predictability of our world.
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.
sktime - A unified framework for machine learning with time series
uncertainty-baselines - High-quality implementations of standard and SOTA methods on a variety of tasks.
darts - A python library for user-friendly forecasting and anomaly detection on time series.