Your question is Time Series Feature Engineering. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
You are working on a forecasting task where past values, seasonality, and recent trends all matter. The raw data is noisy, irregular in places, and the useful signal is spread across time.
How would you approach feature engineering for a time series forecasting problem?