NeuralProphet: Explainable Forecasting at Scale · HackerPapers discussions

NeuralProphet: Explainable Forecasting at Scale — mentioned by maxmc

Thu, 18 Aug 2022 00:17:31 GMT

Figure 7 of the mentioned paper evaluates FB-Prophet in a extremely convenient environment of long horizon h in {30,60,90,120,150,180}. It is known that ETS and ARIMA models concatenate errors and degrade in performance with longer forecasting horizons. We have explored and offered solutions to these issues with the N-HiTS model specialized in long-horizon (https://arxiv.org/abs/2201.12886). In recent years, FB-Prophet has gained a reputation for the poor quality of its predictions in many practical scenarios (short/medium term horizon) and its slow performance on bigger data sets; our…

Paper and discussion history. A mention is not an endorsement.

NeuralProphet: Explainable Forecasting at Scale — mentioned by maxmc

Wed, 17 Aug 2022 23:43:39 GMT

Be surprised. Here is the original Neuralprophet paper: https://arxiv.org/pdf/2111.15397.pdf It only compares itself to Prophet.

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NeuralProphet: Explainable Forecasting at Scale — mentioned by fedegr

Wed, 17 Aug 2022 23:40:50 GMT

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/).

Paper and discussion history. A mention is not an endorsement.