DY0-001 · Question #59
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…
The correct answer is A. Autoregressive. An autoregressive model uses past values of the series itself (here, historical daily copper prices) as predictors for future values, making it the most suitable technique when only the time-series history is available.
Question
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?
Options
- AAutoregressive
- BMoving average
- CDynamic time warping
- DRelative strength
How the community answered
(27 responses)- A81% (22)
- B4% (1)
- C11% (3)
- D4% (1)
Explanation
An autoregressive model uses past values of the series itself (here, historical daily copper prices) as predictors for future values, making it the most suitable technique when only the time-series history is available.
Topics
Community Discussion
No community discussion yet for this question.