Exponential moving averages (emas) are a powerful means of detecting changes in time series data. However, if you are new to this task, you may be wondering how to choose from conflicting advice about how to calculate emas. This tip reviews several of the most popular methods for calculating moving averages. Additionally, this tip presents T-SQL code samples with common table expressions and stored procedures for generating emas from an underlying time series dataset.
“Emas don’t just track trends—they reveal momentum in motion.” That’s why they’re favored when recent values matter most—and why this tip focuses on helping you calculate them with precision.
Read on for the formula and a couple of lengthy scripts to generate it.