The question describes a univariate time series forecasting problem where future values of a variable (copper price) are to be predicted based solely on its own past values. An autoregressive (AR) model is the most appropriate technique for this scenario. An AR model is a regression model that uses the dependent relationship between an observation and a number of lagged observations (i.e., its own past values) to forecast future values. This directly matches the data scientist's approach of using only the historical daily price of copper as input.

B. Moving average: This is a calculation to analyze data points by creating a series of averages of different subsets of the full data set. It is primarily used for smoothing data to identify trends, not as a direct forecasting model in this context.

C. Dynamic time warping: This is an algorithm used to measure the similarity between two temporal sequences, which may vary in speed. It is applied in classification or clustering, not for forecasting a single time series.

D. Relative strength: This is a specific technical momentum indicator used in financial analysis to measure the magnitude of recent price changes. It is a derived metric, not a general forecasting technique for the price itself.

1. Pennsylvania State University

STAT 510: Applied Time Series Analysis. (n.d.). Lesson 1.2: The AR Model. Eberly College of Science. Retrieved from https://online.stat.psu.edu/stat510/lesson/1/1.2. The courseware defines: "An autoregressive model is when a value from a time series is regressed on previous values from that same time series." This directly supports the choice of Autoregressive for the described task.

2. Box

G. E. P.

Jenkins

G. M.

Reinsel

G. C.

& Ljung

G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). John Wiley & Sons. In Chapter 3

"Linear Stationary Models

" Section 3.1.1

"The Autoregressive Process

" the text formally defines the AR(p) model where the current value of the process is expressed as a finite

linear aggregate of previous values of the process and a random shock.

3. Hyndman

R. J.

& Athanasopoulos

G. (2018). Forecasting: Principles and Practice (2nd ed.). OTexts. In Chapter 8

Section 8.3

"Autoregressive (AR) models

" it is stated

"In an autoregression model

we forecast the variable of interest using a linear combination of past values of the variable."

4. Berndt

D. J.

& Clifford

J. (1994). Using dynamic time warping to find patterns in time series. In KDD workshop (Vol. 10

No. 16

pp. 359-370). This foundational paper describes DTW as a method for providing "a distance measure between two time series" (p. 360)

establishing its role in similarity measurement rather than forecasting.