Business Analytics: Forecasting with Seasonal Baseline Smoothing

Seasonal exponential smoothing is an extension of simple exponential smoothing (SES). Seasonal smoothing is often used when a baseline shows regular seasonal peaks and valleys. Residential water usage is a familiar example: consumption rises during the summer and fall and drops during winter and spring—but the overall annual consumption tends to remain stationary over several years. In this course, veteran business analytics consultant and instructional expert Conrad Carlberg shows how to incorporate seasonal variation for more accurate and insightful forecasts. Learn how to identify seasonality, perform seasonal smoothing of horizontal baselines, and optimize your forecasts with R and Microsoft Excel.

Learning objectives
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.