[D] Fool me once, shame on you; fool me twice, shame on me: Exponential Smoothing vs. Facebook's Neural-Prophet.

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https://preview.redd.it/put2itbz1bi91.png?width=920&format=png&auto=webp&s=10c905054f14214d1caaaf7765dc5693efad4a14

History tends to repeat itself. But FB-Prophet's [tainted memory](https://www.reddit.com/r/MachineLearning/comments/syx41w/p_beware_of_false_fbprophets_introducing_the/) is too recent and should act as a warning not to repeat the same mistakes.

This post compares Neural-Prophet's performance with Exponential Smoothing (ETS), a half-century-old forecasting method part of every practitioner's toolkit.

Our [comparison](https://github.com/Nixtla/statsforecast/blob/main/experiments/neuralprophet/README.md) covers Tourism, M3, M4, ERCOT, and ETTm2 datasets, following the authors' recommended hyperparameter and network configuration settings. Despite Neural-Prophet's [outstanding success](https://arxiv.org/abs/2111.15397) over its unreliable predecessor, its errors are still 30 percent larger than ETS' …

facebook machinelearning prophet