xplainable
statsforecast
| xplainable | statsforecast | |
|---|---|---|
| 2 | 58 | |
| 52 | 3,575 | |
| - | 3.0% | |
| 9.0 | 8.9 | |
| 14 days ago | 7 days ago | |
| Python | Python | |
| GNU Affero General Public License v3.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
xplainable
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Explainable (Structured) Machine Learning Algorithm
Just for some respite from the discussion of our soon-to-be AI overlords (LLMs), I'm one of the contributors to an open-source Python package, Xplainable (https://github.com/xplainable/xplainable). Xplainable is a novel (structured) machine learning algorithm that's inherently explainable, as opposed to being a post-hoc explainer (like SHAP or Lime).
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Tools for documenting OS Python Package
I'm looking at migrating the docs for our open-source Python package https://github.com/xplainable/xplainable from sphinx to something else. I was initially looking at either docosaurus or Mintlify. Mintlify looks substantially easier to setup but I'm questioning the extensibility (also the cost).
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?
darts - A python library for user-friendly forecasting and anomaly detection on time series.
mlforecast - Scalable machine 🤖 learning for time series forecasting.
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
nixtla - Python SDK for TimeGPT, a foundational time series model
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
fable - Tidy time series forecasting
flow-forecast - Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
pytorch-forecasting - Time series forecasting with PyTorch
tsfeatures - Calculates various features from time series data. Python implementation of the R package tsfeatures.
hts - Hierarchical and Grouped Time Series
Numba - NumPy aware dynamic Python compiler using LLVM
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️