A data scientist is building a forecasting model for the price of copper.

→ An Autoregressive (AR) model is ideal when past values of a time series are used to predict future values. Since the only input is historical price data of copper, AR is the most appropriate technique.

Why the other options are incorrect:

B: Moving average smooths noise but doesn’t model the dependencies for prediction.

C: Dynamic time warping is used for measuring similarity between time series, not forecasting.

D: Relative strength is a financial metric used for comparing asset performance — not a forecasting technique.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 3.5:“Autoregressive models are used when the goal is to predict future values based solely on past values in a univariate time series.”

Time Series Analysis and Forecasting, Chapter 5:“AR models capture the temporal dependencies in time series data and are foundational in time-based prediction.”

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