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I notice that nobody has had the opportunity to consider my questions above.
I hope they are not too vague/obscure?
I've been through the various documentation regarding timeseries, and the maths quickly becomes hieroglyphics.
In simple terms, I was hoping that somebody might be able to explain to me the relationship between aggregation and forecasting.
When I forecast forward using hourly data, I get a good pattern for what happens over a day, but it seems to lose all fidelity with respect to longer time periods.
Conversely, if I aggregate up to weekly data, I get some seasonality, but I lose that day on day level of detail.
What is a good rule of thumb here? I notice that the forecast periods toggle only goes to 365. I have three years of hourly data here and I would like to project forward say six months. Seems reasonable?
Don't really know how to think about it and could do with some guidance if anyone feels so inclined…
This isn't really a technical issue, rather it relates to the decision the forecast is being used to address. If the issue being addressed is "How many web servers should we have spun-up?", then the time interval of the forecast should match the time it takes to spin-up or spin-down servers (my hunch this will correspond to an hourly model). On the other hand, if the question relates to "how large an inventory should we have on hand?" then an hourly time interval would be too granular, and you would want one on the scale of inventory replenishment (my hunch this would be weekly or monthly).
If the question being addressed is the former, then I would think about using an ARIMA model with covariates. The covariates to use would be oriented towards the macro level seasonal effects (e.g., Christmas rush and the summer dip, which likely could be addressed using month indicator variables) and the day of week effects (day of week indicator variables would handle this) while the ARIMA methods would handle the time of day effects.