How do you approach a time-series forecasting problem, and what is special about validating it?
Time series breaks the usual ML assumptions: data is ordered and correlated, so random cross-validation silently leaks the future and inflates your score. What matters is decomposition, point-in-time features, and time-aware validation.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Time series breaks the usual ML assumptions: data is ordered and correlated, so random cross-validation silently leaks the future and inflates your score. What matters is decomposition, point-in-time features, and time-aware validation.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
