statsforecast

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/abs/2111.15397?fbclid=IwAR2vCkHYiy5yuPPjWX...). We noted that the paper only compares NeuralProphet against Prophet and does not include standard time series datasets (such as M-competitions). So we decided to test the model against simpler models (ETS in this case) using the StatsForecast library (https://github.com/Nixtla/statsforecast/).

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 created on Facebook). We can conclude from the results comparing AutoARIMA vs. Prophet (https://github.com/Nixtla/statsforecast/tree/main/experiment..., using the same datasets as in the ETS vs. NeuralProphet experiment) that ETS is also better than Prophet. Regarding NeuralProphet vs. Prophet, the results are not conclusive for these datasets.

We benchmarked on more than 55K series and show that ETS improves MAPE and sMAPE forecast accuracy by 32% and 19%, respectively, with 104x less computational time over NeuralProphet. We hope this exercise helps the forecast community avoid adopting yet another overpromising and unproven forecasting method.