- [Instructor] I'm in exercise file 01_04…on the worksheet named Seasonal Effect Example.…To build a forecast that responds…to seasonality in the baseline…we need to start by defining seasonal effects.…A seasonal effect measures the amount…of the effect that being in a given season exerts…on values in the baseline.…In this case, simply being in the season named December…might increase the December sales by $10,000…over the average monthly sales for the year.…
Similarly, taking place in the month of July…might decrease July sales by $3,000…below the average monthly sales for the year.…Then the seasonal effect for December would be plus 10,000,…and the seasonal effect for July would be minus 3,000.…Let's see how that works out in practice.…On the worksheet that you see,…I have calculated the mean sales…for the first year in cell M4.…I gave the cell the name Year_1_Mean, as shown in cell L4.…
So the seasonal effect for the first period,…in cell J2, subtracts the year one mean…from the actual observation for period one in cell D2.…
- Identify what distinguishes seasonality from a trend or a cycle.
- Explore how to use absolute and relative references in defined names, and recall that absolute reference always remain static while relative references change depending on precedent.
- Identify seasonality in a baseline by examining autocorrelation functions in a correlogram.
- Explore how to initialize seasonal effects in a baseline.
- Forecast the current level of the baseline and the current seasonal effect from prior observations, forecasts, and smoothing constants.
- Quantify a measure of the aggregate error in a forecast, and minimize it using Solver.
- Establish a baseline in a data object and forecast from that baseline in R.
- Compare Excel and R results.