Book Image

Data Smart

By : John W. Foreman
Book Image

Data Smart

By: John W. Foreman

Overview of this book

Data Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions. But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope. Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, math and the magic, behind big data.
Table of Contents (18 chapters)
Free Chapter
1
Cover
2
Credits
3
About the Author
4
About the Technical Editors
5
Acknowledgments
18
End User License Agreement

Starting Slow with Simple Exponential Smoothing

Exponential smoothing techniques base a future forecast off of past data where the most recent observations are weighted more than older observations. This weighting is done through smoothing constants. The first exponential smoothing method you're going to tackle is called simple exponential smoothing (SES), and it uses only one smoothing constant, as you'll see.

Simple exponential smoothing assumes that your time series data is made up of two components: a level (or mean) and some error around that level. There's no trend, no seasonality, just a level around which the demand hovers with little error jitters here and there. By preferring recent observations, SES can account for shifts in this level. In formula-speak then, you have:

  1. Demand at time t = level + random error around the level at time t

And the most current estimate of the level serves as a forecast for future time periods. If you're at month 36, what&apos...