How should the developer verify the suitability of an ARIMA approach?
Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Impute hourly missing data. Perform a Seasonal Trend decomposition.
Use Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. Choose ARIMA as the machine learning (ML) problem. Check the model performance.
Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Resample data by using the aggregate daily total. Perform a Seasonal Trend decomposition.
Use Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. Impute missing hourly values. Choose ARIMA as the machine learning (ML) problem. Check the model performance.
Explanations:
Amazon SageMaker Data Wrangler is useful for data preprocessing, but performing a Seasonal Trend decomposition without resampling data to daily totals is not the best approach for verifying ARIMA suitability for daily demand forecasting. ARIMA typically requires data to be at a consistent frequency, and imputing missing hourly data would be more directly related to time series modeling.
Amazon SageMaker Autopilot is not the best choice here because ARIMA is a time series-specific method, and Autopilot does not offer direct support for time series problems like ARIMA. Additionally, missing data handling and ARIMA suitability verification are not straightforward in Autopilot.
Resampling data by aggregating the hourly demand data into daily totals is the correct preprocessing step for verifying ARIMA suitability. ARIMA models are most commonly used for daily or weekly aggregated data, and performing Seasonal Trend decomposition is a valid technique for analyzing seasonality and trends in time series data.
While imputing missing data is important, Amazon SageMaker Autopilot does not provide direct support for ARIMA time series modeling. Furthermore, ARIMA suitability should be verified on resampled daily data, not hourly data with missing values.
If memory serves me right, the answer is:
Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Resample data by using the aggregate daily total. Perform a Seasonal Trend decomposition.