Topic 1 Question 69
A large consumer goods manufacturer has the following products on sale:
- 34 different toothpaste variants
- 48 different toothbrush variants
- 43 different mouthwash variants The entire sales history of all these products is available in Amazon S3. Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products. The company wants to predict the demand for a new product that will soon be launched. Which solution should a Machine Learning Specialist apply?
Train a custom ARIMA model to forecast demand for the new product.
Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product.
Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
Train a custom XGBoost model to forecast demand for the new product.
解説
The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series. They then use that model to extrapolate the time series into the future. Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html
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コメント(6)
B https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html "...When your dataset contains hundreds of related time series, DeepAR outperforms the standard ARIMA and ETS methods. You can also use the trained model to generate forecasts for new time series that are similar to the ones it has been trained on."
👍 17HaiHN2021/10/19It is B
👍 3hans12342021/10/13- 正解だと思う選択肢: B
DeepAr for new products forever!
👍 3Valcilio2023/03/08
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