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

Holt's Trend-Corrected Exponential Smoothing

Holt's Trend-Corrected Exponential Smoothing expands simple exponential smoothing to create a forecast from data that has a linear trend. It's often called double exponential smoothing, because unlike SES, which has one smoothing parameter alpha and one non-error component, double exponential smoothing has two.

If the time series has a linear trend, you can write it as:

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

The most current estimates of the level and trend (times the number of periods out) serve as a forecast for future time periods. If you're at month 36, what's a good estimate of demand at time period 38? The most recent level estimate plus two months of the trend. And time 40? The level plus four months of the trend. Not as simple as SES but pretty close.

Now, just as in simple exponential smoothing, you need to get some initial estimates of the level and trend values, called...