[P] Beware of false (FB-)Prophets: Introducing the fastest implementation of auto ARIMA [ever].

• neuralforecast

  84 2,405 8.9 Python

  Scalable and user friendly neural :brain: forecasting algorithms.

  

Hi, are you referring to the link in the paper? It is based on our NeuralForecast library (https://github.com/Nixtla/neuralforecast). You can install all our libraries using pip and conda, and the API is quite similar to sklearn (train and forecast). :)

• statsforecast

  58 3,540 8.9 Python

  Lightning ⚡️ fast forecasting with statistical and econometric models.

  

Please check it out and give us a star if you like it https://github.com/Nixtla/statsforecast.

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• Numba

  124 9,432 9.9 Python

  NumPy aware dynamic Python compiler using LLVM

  

Inspired by this, we translated Hyndman's auto.arima code from R and compiled it using the numba library. The result is faster than the original implementation and more accurate than prophet .

• pytorch-forecasting

  9 3,590 8.7 Python

  Time series forecasting with PyTorch

To name a few: https://github.com/jdb78/pytorch-forecasting, https://github.com/unit8co/darts, https://github.com/Nixtla/neuralforecast

• darts

  47 7,272 9.1 Python

  A python library for user-friendly forecasting and anomaly detection on time series.

  

To name a few: https://github.com/jdb78/pytorch-forecasting, https://github.com/unit8co/darts, https://github.com/Nixtla/neuralforecast

• nixtla

  8 1,411 9.5 Jupyter Notebook

  Python SDK for TimeGPT, a foundational time series model

  

Yes, for example we have this paper in long-horizon settings using our library NeuralForecast and this experiment with other of our libraries MLForecast, both of them outperforming autoarima.

• mlforecast

  11 713 8.8 Python

  Scalable machine 🤖 learning for time series forecasting.

  

Yes, for example we have this paper in long-horizon settings using our library NeuralForecast and this experiment with other of our libraries MLForecast, both of them outperforming autoarima.

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• Puts Debuggerer

  3,150 91 5.5 Ruby

  Ruby library for improved puts debugging, automatically displaying bonus useful information such as source line number and source code.

It could be interesting, but make it a proper Python package and follow the sklearn interface. It requires very little effort (once you know how). It is not inviting, if it is installed by custom commands and then only offers an opinionated evaluation on self-selected datasets. It would be much more convincing if one could do pip install git+https://github.com/... and then use .fit and .predict methods which everyone is familiar with. People would test it on their own data sets. Testing on the paper's dataset does not mean much - just as it didn't for Prophet.

Suggest a related project