- [Instructor] Go ahead and open up 01_03_ARIMA…from the exercise files before starting this video.…Next, open up exercise file 01_03.…I'm in the worksheet called Baseline.…In the course of working on a forecasting project,…you're often confronted with…a decision regarding a baseline.…When you chart it, the baseline…might display seasonal components,…but then again, it might be just an optical illusion.…I think it's generally a good idea…to have a look at the charted baseline,…but it's also a good idea to back up your visual judgment…with a quantitative test.…
Here's one such test in exercise file 01_03.…This baseline consists of 20 quarterly observations,…and it's pretty clear that it's seasonal.…The baseline spikes during the first quarter of…each year when t equals one, five, nine, 13, and 70.…Many of the seasonal baselines that you run into…will not be anywhere near as evident as this one.…Here's one way that you can get a better idea than…just by eyeballing a chart at the baseline.…
Click the Add Ins tab on the ribbon.…
- 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.