Question

\begin{tabular}r \hline \multicolumn{1}c{ A } & \multicolumn{1}{ B } \\ \hline \multicolumn{1}{ Date } & \multicolumn{1}l{ Sales } \\ \hline Oct-11 & 16,402 \\ \hline Jan-12 & 21,288 \\ \hline Apr-12 & 16,867 \\ \hline Jul-12 & 16,779 \\ \hline Oct-12 & 16,929 \\ \hline Jan-13 & 22,726 \\ \hline Apr-13 & 16,706 \\ \hline Jul-13 & 17,117 \\ \hline Oct-13 & 17,258 \\ \hline Jan-14 & 21,515 \\ \hline Apr-14 & 17,050 \\ \hline Jul-14 & 17,406 \\ \hline Oct-14 & 17,732 \\ \hline Jan-15 & 21,751 \\ \hline Apr-15 & 17,119 \\ \hline Jul-15 & 17,427 \\ \hline Oct-15 & 17,613 \\ \hline Jan-16 & 21,626 \\ \hline Apr-16 & 16,196 \\ \hline Jul-16 & 16,169 \\ \hline Oct-16 & 16,441 \\ \hline Jan-17 & 20,690 \\ \hline Apr-17 & 16,017 \\ \hline Jul-17 & 16,429 \\ \hline Oct-17 & 16,667 \\ \hline Jan-18 & 22,766 \\ \hline Apr-18 & 16,781 \\ \hline Jul-18 & 17,776 \\ \hline Oct-18 & 17,821 \\ \hline Jan-19 & 22,977 \\ \hline Apr-19 & 17,627 \\ \hline \end{tabular} Using the procedures detailed in the chapter, decompose the time series into the trend, seasonality, and irregular components. .. Which of the time series methodologies discussed in the chapter would likely be the most appropriate for forecasting Target's sales?

Answer 1

To decompose the time series into trend, seasonality, and irregular components for Target's sales, models like Exponential Smoothing or ARIMA are appropriate. Sales predictions using the regression model are 250.2 thousand dollars for day 60, and 324.12 thousand dollars for day 90.

Step-by-step explanation:

The decomposition of a time series into trend, seasonality, and irregular components involves separating the long-term pattern, the repetitive and predictable movement within a year, and the random fluctuations respectively. In the context of forecasting Target's sales, models like Exponential Smoothing or ARIMA could be suitable as they account for trend and seasonality in a time series.

Using the given regression model î = 101.32 + 2.48x for sales predictions:

• On day 60, the predicted sales would be: î = 101.32 + (2.48 × 60) = 250.2 thousand dollars.
• On day 90, the predicted sales would be: î = 101.32 + (2.48 × 90) = 324.12 thousand dollars.