SARIMA vs. LightGBM for Forecasting with External Variables

WHAT THIS TESTS: This question assesses your practical experience and pragmatism. It's not a quiz on model definitions. The interviewer wants to see if you can move beyond pure technical merit and make a decision based on real-world constraints like business needs (interpretability vs. accuracy), data complexity (number of external variables), and engineering cost (implementation and maintenance).

A GOOD ANSWER COVERS: An excellent answer is structured around the trade-offs. First, state that the 'best' model depends on the specific business problem. Second, discuss SARIMA, highlighting its strength in interpretability (clear coefficients) and its statistical foundation, but noting its weakness with many (>10) non-linear variables and its requirement for stationary data. Third, discuss LightGBM, emphasizing its ability to handle hundreds of features and capture complex, non-linear interactions, but acknowledging its 'black box' nature and risk of overfitting. Finally, directly compare them on the three axes: interpretability (SARIMA wins), performance (LightGBM usually wins with complex data), and implementation (SARIMA's statistical setup vs. LightGBM's feature engineering and tuning).

COMMON WRONG ANSWERS: A major red flag is declaring one model universally superior, e.g., "LightGBM is always better because it's modern ML." This shows a lack of practical experience. Other mistakes include focusing only on predictive accuracy (like RMSE) while ignoring business context or implementation costs, not knowing how to adapt a non-time-series model like LightGBM for forecasting (e.g., by creating lag features), or being unable to articulate the specific pain points of using SARIMAX with a large number of external variables.

LIKELY FOLLOW-UPS: Expect questions like: "How would you make the LightGBM model more interpretable?" (Answer: SHAP, LIME, partial dependence plots). "You have 500 external features for your forecast; which model do you start with and why?" (Answer: LightGBM, as SARIMAX is intractable). "Your stakeholder is the CFO, who needs to explain the forecast to the board. Which model do you lean towards?" (Answer: SARIMAX or a simpler linear model, prioritizing explainability). "Have you considered a hybrid approach?" (Answer: Yes, using SARIMA for the baseline trend/seasonality and an ML model for the residuals).

ONE CONCRETE EXAMPLE: Imagine forecasting daily sales for an e-commerce site with data on 150 different marketing campaigns, competitor prices, and holiday events. A SARIMAX model would be nearly impossible to specify correctly; you can't realistically manage 150+ exogenous variables and their interactions. You might pick 5-10, losing significant information. A LightGBM model, however, is designed for this. You can feed it all 150+ features as-is. It will automatically find the most predictive features and their non-linear interactions, likely resulting in a much more accurate forecast. The trade-off is that if a marketing manager asks "What is the exact sales lift from campaign X?", SARIMAX gives a direct coefficient, while with LightGBM you'd provide a SHAP value, which is an approximation of marginal contribution.