A developer has hourly historical demand data in Amazon S3, but some hours are missing. The developer wants to check if an ARIMA model is suitable. What is the best way to verify ARIMA suitability?

The correct approach is to resample the data to a daily aggregate and then perform a seasonal-trend decomposition. ARIMA models are sensitive to missing values and often perform better on regularly spaced, aggregated data. Resampling to daily totals effectively handles the missing hourly data by aggregating available information and creating a consistent time series. Seasonal-trend decomposition then helps identify seasonality, trend, and residuals, which are crucial for determining if an ARIMA model (which models these components) is appropriate. Using Data Wrangler to impute missing hourly values might introduce bias if the imputation method isn't robust, and it doesn't address potential issues with high-frequency data for ARIMA. SageMaker Autopilot is for automated model training, not for initial suitability assessment of a specific model type like ARIMA, nor does it directly perform seasonal-trend decomposition for this purpose.