Question

What method should be employed for generating predictions for each month in 2003, based on the data from 1997-2002, without specifying the length of the seasonality?

a) Linear regression
b) Exponential smoothing
c) AutoRegressive Integrated Moving Average (ARIMA)
d) Multiple regression

Answer 1

To generate predictions for each month in 2003 based on past data without a known seasonality length, the ARIMA method is best suited, as it effectively captures past data patterns.

Step-by-step explanation:

To predict values for each month in 2003 based on data from 1997-2002 without specifying the length of the seasonality, the AutoRegressive Integrated Moving Average (ARIMA) method would be most suitable.

ARIMA is capable of capturing patterns from past data including trends and seasonality, even when the seasonality length is unknown.

Other methods like linear regression or exponential smoothing might not be able to capture the seasonal variations as effectively as ARIMA, while multiple regression would require clearly defined independent variables, which are not specified in the scenario.