Case study - Insight Manufacturing
1. Analyze the historical data, and from that analysis, recommend an appropriate forecasting method for each product. For methods that require a selection of a number of periods, or constants such as weightings or α, you can randomly select typical values for your method.
2. Using your selected forecasting methods, generate forecasts for the historical data and then use some standard measures of forecast accuracy to evaluate your methods. You can experiment with different numbers of periods and/or weights and values of α to see if you can improve the accuracy.
3. Discuss why certain sets of data are better forecast with different methods.