- [Instructor] I'm in exercise file 0203.…What you see on your screen right now…is a forecast made on a different baseline…using techniques that we have already explored…in this course so far.…I'd like to direct your attention in particular…to the alpha and the delta smoothing constants.…You might want to make a note of those…because we're about to run the same analysis…on the same data set using R.…So we'll be in a position to compare the results…that Excel returns with the results that are returned by R.…
We're gonna start out by highlighting or selecting…the baseline data because we'll be pulling that data…into R using the desk tools that function in R…but with that highlighted, now we can start R…and we'll need to pull in a couple of packages…in order to complete the analysis.…The first one is going to be desk tools…which you'll pull in using the library command.…And we'll also pull in the forecast function…package rather, using again the library command.…
With those in hand, we can start to perform the analysis.…
- 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.