December 2016
Beginner to intermediate
864 pages
24h 44m
English
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Chapter 63. Winters method
You often need to predict future values of a time series, such as monthly costs or monthly product revenues. This is usually difficult because the characteristics of any time series are constantly changing. Exponential smoothing or adaptive methods are usually best suited for forecasting the future values of a time series. In this chapter, I describe the most powerful smoothing method: Winters method (attributed to Peter Winters and Charles Holt). To help you understand how Winters method works, I’ll use it to forecast monthly housing starts in the United States. Housing starts are simply the number of new homes whose construction begins during a month. I’ll begin by describing the three key characteristics of a time ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.