CompTIA DataX (DY0-001) — Question 22

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?

Answer options

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

Correct answer: A

Explanation

The Autoregressive method is appropriate here because it uses previous price data to predict future prices, making it ideal for time series forecasting. The Moving average method smooths the data but does not leverage past values as effectively for prediction. Dynamic time warping is more suited for comparing time series data rather than forecasting, and Relative strength is a technical analysis tool, not a forecasting technique.