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Time Series Forecasting • Statistical Models (ARIMA, Exponential Smoothing)Medium⏱️ ~3 min

Choosing Between ETS, ARIMA, and Alternatives

Decision Framework
Choose ETS for clear visual trend/seasonality. Choose ARIMA for subtle autocorrelation patterns that statistical tests detect but you cannot see by eye.
ETS STRENGTHS
Simple to explain ("recent data weighted more"). Handles missing values gracefully. Works with limited data (2 seasonal cycles). Interpretable components—plot level, trend, seasonal separately. Faster to fit.
ARIMA STRENGTHS
Captures complex autocorrelation ETS misses. Better when today strongly depends on past values or past shocks. More flexible—models patterns ETS cannot. SARIMA handles multiple seasonalities.
💡 Insight: ETS and ARIMA often produce similar forecasts. The choice matters for edge cases. When unsure, fit both and compare cross-validation error.
WHEN TO USE DEEP LEARNING
Neural networks shine when: many related series (patterns transfer), external features matter (weather, promotions), patterns are nonlinear. Need thousands of points and hours to train. For single univariate series, statistical models usually win.
ALWAYS BENCHMARK BASELINES
Naive: tomorrow = today. Seasonal naive: tomorrow = same day last year. If fancy models do not beat these, something is wrong. Baselines are surprisingly hard to beat on noisy data.
⚠️ Warning: Do not pick models by in-sample fit. Use out-of-sample cross-validation—train on past, test on unseen future.

💡 Key Takeaways

✓ETS for clear visual trend/seasonality; ARIMA for complex autocorrelation patterns

✓ETS is simpler, handles missing values, needs less data; ARIMA is more flexible

✓Deep learning needs thousands of points, hours to train—wins only with many related series

✓Always benchmark against naive baselines; if you cannot beat them, something is wrong

✓Use out-of-sample cross-validation, not in-sample fit, to select models

📌 Interview Tips

1When unsure between ETS and ARIMA, fit both and compare cross-validation error

2Mention that naive baselines are surprisingly hard to beat on noisy data

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