Topic 1 Question 216
A data scientist at a retail company is forecasting sales for a product over the next 3 months. After preliminary analysis, the data scientist identifies that sales are seasonal and that holidays affect sales. The data scientist also determines that sales of the product are correlated with sales of other products in the same category.
The data scientist needs to train a sales forecasting model that incorporates this information.
Which solution will meet this requirement with the LEAST development effort?
Use Amazon Forecast with Holidays featurization and the built-in autoregressive integrated moving average (ARIMA) algorithm to train the model.
Use Amazon Forecast with Holidays featurization and the built-in DeepAR+ algorithm to train the model.
Use Amazon SageMaker Processing to enrich the data with holiday information. Train the model by using the SageMaker DeepAR built-in algorithm.
Use Amazon SageMaker Processing to enrich the data with holiday information. Train the model by using the Gluon Time Series (GluonTS) toolkit.
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コメント(6)
- 👍 2drcok872023/02/12
- 正解だと思う選択肢: B
Amazon Forecast is an AWS service that uses machine learning to build accurate time-series forecasts. It provides several built-in algorithms that support holiday featurization, and the DeepAR+ algorithm can handle the seasonality and correlation with other products with minimal development effort. With Amazon Forecast, the data scientist can easily configure the forecast horizon, select the appropriate forecast frequency, and configure the model training to incorporate the available historical data. Using Amazon SageMaker Processing to enrich the data with holiday information may require more development effort and does not offer the same level of automation and integration as Amazon Forecast.
👍 2AjoseO2023/02/17 - 正解だと思う選択肢: B
It is deepAR
👍 2Chelseajcole2023/03/06
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