Neural Prophet: Bridging the Gap Between Accuracy and Interpretability

As we increase the number of auto-regressors, computational time remains constant for AR-Net while increasing quadratically for Classic AR. This figure is from the original AR-Net paper.
  1. Neural Prophet Github: https://github.com/ourownstory/neural_prophet
  2. R.J. Hyndman and G. Athanasopoulos. Forecasting: principles and practice
  3. Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. Sequence to sequence learning with neural networks. In Proceedings of the 27th International Conference on Neural Information Processing Systems — Volume 2, NIPS’14, pages 3104–3112, Cambridge, MA, USA, 2014. MIT Press.
  4. G. Montavon, W. Samek, and K.-R. Muller. Methods for interpreting and understanding deep neural networks. arXiv preprint arXiv:1706.07979, 2017
  5. AR-Net: https://arxiv.org/pdf/1911.12436.pdf
  6. Facebook Prophet: https://facebook.github.io/prophet/

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Engineer turned Data Scientist.

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Alec Delany

Alec Delany

Engineer turned Data Scientist.

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