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#general
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# general
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Andrei Tulbure

11/22/2022, 1:26 PM
Hi. Sorry for bothering. Quick question: I tried running auto_arima_prophet in the same setup we have used Prophet for our forecasting and we have seen that auto_arima_prophet has 7x the running time for fit and predict, that Prophet has. Is there something we are doing wrong ? MSE is still 10% better for ARIMA, but still, I find it weird.
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Max (Nixtla)

11/22/2022, 3:02 PM
Hi! For production settings we recommend using the forecast method instead of the fit predict ones.
Also, you should explore the possibility of using other models like MSTL or ETS. This newly released guide might be helpful. https://nixtla.github.io/statsforecast/examples/getting_started_complete.html
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Andrei Tulbure

11/23/2022, 7:55 AM
@Max (Nixtla) https://github.com/Nixtla/statsforecast/tree/main/experiments/arima_prophet_adapter -> I see you here using fit and then make future dataframe and then predict. Could you be more specific what you mean by using forecast?
Secondly, we want to test, on our dataset, how much of a speed and accuracy improvement we get
?
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Max (Nixtla)

11/23/2022, 5:09 PM
Hi Andrei, I will try to give you a complete answer soon.
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Do you have any code that you could share so i can be more specific?
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Andrei Tulbure

11/23/2022, 5:11 PM
Yes, I will ask the guys from our team to send over the code. But most plainly put. compared directly Autoarimaprophet with prophet, with uncert intervals = 0 and some fut regressors. The dataset is weekly and kinda small.
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Max (Nixtla)

11/23/2022, 5:13 PM
Is there a specific reason you want to compare Prophet and AutoArima? I would highly suggest finding appropriate models for each combination of series (e.g. product and store). For some series, sparse models like ADIDA or Croston might be very successful.
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Andrei Tulbure

11/23/2022, 5:14 PM
we have weekly data and it`s not sparse. And we want to use it because 1. we have seasonality and trend + maybe some holidy effect (product demand forecasting). we have some future regressors and dataset is not that large to use deep learning models
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Max (Nixtla)

11/23/2022, 5:20 PM
For that use case, I suggest comparing Prophet against MSTL. MSTL decomposes seasonality and trends but can fit multiple seasonalities. It’s also faster than ARIMA. That’s good for your use case because with weekly data, you might find monthly patterns (last week of the month) as well as yearly patterns (last week of December)
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Would you mind looking at this guide and telling me if that’s something you would try?
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