Viewing profile — fedegr

I completely agree with your perspective. It is a reality that deep learning models might offer certain advantages over classical statistical models. We are building benchmarks and…

Co-author here: all in due time. Next iteration we will include LigthGBM, XGBoost, and newer DL models like TFT and NHiTS.

SeasonalNaive is 20% more accurate than Prophet or NeuralProphet and 366 times faster for next-day electricity demand forecasting.

NeuralProphet is the successor-extension of Prophet, and it aims to provide Prophet with neural networks and autoregressive terms. The paper can be found here ( https://arxiv.org/a…

I think the problem arises from the datasets used to evaluate the performance of the models. In the case of Prophet's paper, only one time series is used (The number of events crea…

Yes. We found evidence that linear regression with feature engineering is better than prophet on Zillow data.

The pipeline we have developed improves the state of the art in the markets you mention in the following aspects: 1. It is a fully automated end-to-end pipeline for forecast genera…

We missed that, sorry. At the moment, for forecasting the pipeline uses the mlforecast library ( https://github.com/nixtla/mlforecast ) that builds upon sklearn, xgboost and lightg…

Thanks for your comments. We agree that in most cases prophet is not a good benchmark; however, we wanted to use it because it is one of the most used libraries in forecasting. For…