Question

Given Jewelry sales data.

1.Summarize the data
2.Plot the data
3.The goal is to forecast next year's sales.
4.Partition the data into training and validation periods
5.Fit a regression model to sales using log(Sales) as the outcome variable (plot the regression fit)
6.Create (and plot) an ACF plot for lag-1 through lag-20
7.Fit an AR model with lag-2 to the forecast errors (plot the model
8.Examine the ACF plot and the estimated coefficients of the ARIMA(2,0,0) model, comment on the regression forecast
9.Compute forecasts for the upcoming 6 months using the regression and AR(2) models
10Add the forecasts to the plots of the actual data

Answer 1

This question involves analyzing jewelry sales data to forecast next year's sales. The steps to address this question include summarizing the data, plotting the data, partitioning the data into training and validation periods, fitting a regression model, creating an ACF plot, fitting an AR model, examining the ACF plot and estimated coefficients of an ARIMA(2,0,0) model, and computing forecasts using both the regression and AR(2) models. Finally, the forecasts can be added to the plots of the actual data.

Step-by-step explanation:

Summary:

This question involves analyzing jewelry sales data to forecast next year's sales. The steps to address this question include summarizing the data, plotting the data, partitioning the data into training and validation periods, fitting a regression model, creating an ACF plot, fitting an AR model, examining the ACF plot and estimated coefficients of an ARIMA(2,0,0) model, and computing forecasts using both the regression and AR(2) models. Finally, the forecasts can be added to the plots of the actual data.

Primary Topic:

Time Series Analysis and Forecasting

SEO Keywords:

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