How would you approach building a time series forecasting model for a dataset with multiple seasonality?
Instruction: Outline your strategy for modeling time series data exhibiting multiple seasonal patterns.
Updated
Example Answer
I would establish which seasonal cycles the history actually supports. Hourly demand might have daily and weekly patterns, but a short history may not support an annual component. I would begin with a simple benchmark before choosing a complex model.
Candidates could include harmonic regression with Fourier terms for each period, a decomposition method that supports multiple seasonalities, or Prophet with justified seasonal components. Standard SARIMA has one seasonal period; handling additional cycles needs another mechanism rather than just more manual tuning. I would compare candidates at the required horizon using rolling origins, including holidays and periods when the pattern shifts. Extra seasonal terms can overfit, so I would retain them only when they add useful evidence.
Make it your own
State the sampling interval, seasonal periods, length of history, and how you would evaluate each component.
Why this works
It matches the model to the supported seasonal structure.
Follow-up to practice
How would you forecast hourly data with daily and weekly cycles?