3 ms·
And now for the great thing ... Prophet uses Stan underneath [1] and thus is built on foundations of 'regular' Bayesian statistics. Andrew Gelman has written ab
by wjnc 7y ago
And now for the great thing ... Prophet uses Stan underneath [1] and thus is built on foundations of 'regular' Bayesian statistics. Andrew Gelman has written about Prophet as well [2].
After reading this blog I am tempted to get the ML for time series book though. I'd love to try and compare some less than trivial examples with covariates involved.
[1] https://peerj.com/preprints/3190/ https://peerj.com/preprints/3190/
[2] https://statmodeling.stat.columbia.edu/2017/03/01/facebooks-prophet-uses-stan/ https://statmodeling.stat.columbia.edu/2017/03/01/facebooks-...
- antman 7y agoA good explanation for prophet that made it click for me was its recreation with python in pymc https://www.ritchievink.com/blog/2018/10/09/build-facebooks-prophet-in-pymc3-bayesian-time-series-analyis-with-generalized-additive-models/ https://www.ritchievink.com/blog/2018/10/09/build-facebooks-...
- objektif 7y agoWhich book are you referring to?
- wjnc 7y agoIn OP [1]. [1] https://machinelearningmastery.com/deep-learning-for-time-series-forecasting/ https://machinelearningmastery.com/deep-learning-for-time-se...
- ganeshkrishnan 7y agoFacebook prophet is really impressive and has gotten us better precision with shorter iterations even with large datasets compared to CNN+lstm. We run it over millions of inventory over years of data and it has given satisfactory results in majority of the cases
- ayayecocojambo 7y agohave you tried xgboost? in our problems none of the LSTM/Fbrpohpet/arima performed better fine-tuned xgboost model.
- ganeshkrishnan 7y agoJust looked it up. Looks super interesting, I will surely give it a shot. Thanks