tablespoon
statsforecast
| tablespoon | statsforecast | |
|---|---|---|
| 9 | 58 | |
| 39 | 3,565 | |
| - | 2.7% | |
| 5.3 | 8.9 | |
| 7 months ago | 8 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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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
statsforecast
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TimeGPT-1
I can't find the TimeGPT-1 model.
LICENSE Apache-2
https://github.com/Nixtla/statsforecast/blob/main/LICENSE
Mentions ARIMA, ETS, CES, and Theta modeling
- Facebook Prophet: library for generating forecasts from any time series data
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Sales forecast for next two years
If you only have historical data: StatsForecast
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Time series and cross validation
I also recommend you check Nixtla's libraries, in particular StatsForecast and HierarchicalForecast. They offer a wide selection of forecasting models, and can work with multiple time series. Given that you're working with many products in a warehouse, I think the hierarchical forecast can be very useful, especially for the short time series (the ones that don't seem to have enough time stamps).
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Demand Planning
If you are mostly worried about time and use python you could try out Nixtla's statsforecast as it is very snappy. https://github.com/Nixtla/statsforecast
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Statistical vs Machine Learning vs Deep Learning Modeling for Time Series Forecasting
I was researching about using deep learning for time series forecasting applications when I came across two experiments by the Nixtla team. They showed that their traditional statistical ensemble (comprised of AutoARIMA, ETS, CES, and DynamicOptimizedTheta) beat a bunch of deep learning models (link) and also the AWS forecast API (link).
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Recommendations for books on working with time series/forecasting problems?
- https://nixtla.github.io/statsforecast/
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XGBoost for time series
Leaving these two repos here for anyone interested in trying decision tree regression or statistical forecasting baselines: - https://nixtla.github.io/mlforecast/ - https://github.com/Nixtla/statsforecast
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[Discussion] Amazon's AutoML vs. open source statistical methods
In this reproducible experiment, we compare Amazon Forecast and StatsForecast a python open-source library for statistical methods.
- Statistical methods outperform Amazon’s ML Forecast
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!
mlforecast - Scalable machine 🤖 learning for time series forecasting.
Numbers-Prophecy - An experiment to demonstrate the biases and predictability of our world.
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
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.
nixtla - Python SDK for TimeGPT, a foundational time series model
sktime - A unified framework for machine learning with time series
tsai - Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
uncertainty-baselines - High-quality implementations of standard and SOTA methods on a variety of tasks.
fable - Tidy time series forecasting