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Question 51 - MLS-C01 discussion

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A power company wants to forecast future energy consumption for its customers in residential properties and commercial business properties. Historical power consumption data for the last 10 years is available. A team of data scientists who performed the initial data analysis and feature selection will include the historical power consumption data and data such as weather, number of individuals on the property, and public holidays.

The data scientists are using Amazon Forecast to generate the forecasts.

Which algorithm in Forecast should the data scientists use to meet these requirements?

A.
Autoregressive Integrated Moving Average (AIRMA)
Answers
A.
Autoregressive Integrated Moving Average (AIRMA)
B.
Exponential Smoothing (ETS)
Answers
B.
Exponential Smoothing (ETS)
C.
Convolutional Neural Network - Quantile Regression (CNN-QR)
Answers
C.
Convolutional Neural Network - Quantile Regression (CNN-QR)
D.
Prophet
Answers
D.
Prophet
Suggested answer: C

Explanation:

CNN-QR is a proprietary machine learning algorithm for forecasting time series using causal convolutional neural networks (CNNs). CNN-QR works best with large datasets containing hundreds of time series. It accepts item metadata, and is the only Forecast algorithm that accepts related time series data without future values. In this case, the power company has historical power consumption data for the last 10 years, which is a large dataset with multiple time series. The data also includes related data such as weather, number of individuals on the property, and public holidays, which can be used as item metadata or related time series data. Therefore, CNN-QR is the most suitable algorithm for this scenario.References:Amazon Forecast Algorithms,Amazon Forecast CNN-QR

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asked 16/09/2024
Solanki Narendra
34 questions
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