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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 quietly leaks the future and inflates your score. The signal is decomposition, point-in-time features, and time-aware validation.

Updated Aug 2026 · Grounded in real Applied AI Engineer 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 quietly leaks the future and inflates your score. The signal is decomposition, point-in-time features, and time-aware validation.

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