Learn how to perform seasonal baseline smoothing with R and Excel and incorporate seasonal variation for more accurate and insightful forecasts.
- [Conrad] You frequently have access to a baseline of data that varies by season. Your company might sell parkas and sales rise in the fall and decline in the spring. Or, suppose you track and forecast the incidence of traffic accidents, which might increase on weekend nights compared to other dates and times. In these and similar cases, you have your hands on a seasonal baseline. There's information in those seasonal variations that can enrich your forecast considerably and help them grow more accurate.
Hi, my name is Conrad Carlberg. This course is designed to help you identify seasonality in your datasets and use it to enhance your forecasts. I'll be using two applications to do so, Microsoft Excel, which is a great platform for seeing what's going on in an analysis, and R, which enables you to structure a complex analysis with just a few commands. Let's get started.
- 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.