Exam DY0-001 topic 1 question 23 discussion

Actual exam question from CompTIA's DY0-001

Question #: 23
Topic #: 1

A data scientist is building a forecasting model for the price of copper. The only input in this model is the daily price of copper for the last ten years. Which of the following forecasting techniques is the most appropriate for the data scientist to use?

A. Autoregressive
B. Moving average
C. Dynamic time warping
D. Relative strength

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Suggested Answer: A 🗳️

by SuntzuLegacy at May 19, 2025, 9:01 a.m.

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SuntzuLegacy

1 month, 1 week ago

Selected Answer: A

Given only a single historical time series (daily copper prices over 10 years) and no other explanatory variables, a standard choice would be an autoregressive (AR) model. AR models are designed specifically to forecast future values of a time series based solely on its own past values. Moving average is also a time-series method but generally used for smoothing or as part of an integrated ARMA/ARIMA model. Dynamic time warping is for measuring similarity between time-series sequences rather than for direct forecasting. Relative strength is more of a technical analysis indicator, not a forecasting method in the traditional sense. Hence, the best answer is: A. Autoregressive

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