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To generate the forecast, we’ll use the <https://nixtla.github.io/statsforecast/models.html#multiple-seasonalities|MSTL> model, which is well-suited for low-frequency data like the one used here. We first need to import it from `statsforecast.models` and then we need to instantiate it. Since we’re using hourly data, we have two seasonal periods: one every 24 hours
in the documentation from anomaly detection why you here conside the hourly frequency as low frequency ?

could you see here also please ,
 what you recommend after getting the anomaly points do you advice to remove them from the data ?? and replace it by what or something else? <@U03619XRWKD>

Sorry, I didnt understand the initial question

It seems there might be a misunderstanding or potential mistake in the documentation regarding the terminology. Typically, "low frequency" data refers to data that is sampled or recorded at a lower rate, such as daily, weekly, or monthly intervals. Conversely, "high frequency" data refers to data that is recorded at a higher rate, such as hourly, minute-by-minute, or second-by-second intervals.,correct it me if i am wrong please .