Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Practical Predictive Analytics

You're reading from   Practical Predictive Analytics Analyse current and historical data to predict future trends using R, Spark, and more

Arrow left icon
Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781785886188
Length 576 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Ralph Winters Ralph Winters
Author Profile Icon Ralph Winters
Ralph Winters
Arrow right icon
View More author details
Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with Predictive Analytics FREE CHAPTER 2. The Modeling Process 3. Inputting and Exploring Data 4. Introduction to Regression Algorithms 5. Introduction to Decision Trees, Clustering, and SVM 6. Using Survival Analysis to Predict and Analyze Customer Churn 7. Using Market Basket Analysis as a Recommender Engine 8. Exploring Health Care Enrollment Data as a Time Series 9. Introduction to Spark Using R 10. Exploring Large Datasets Using Spark 11. Spark Machine Learning - Regression and Cluster Models 12. Spark Models – Rule-Based Learning

Forecasting using ALL AGES

The following code will perform the following steps:

  1. First, it will filter the data so that it only includes the ALL AGES category.

  2. Then, it creates a time series object.

  3. Finally, it runs a simple exponential model, using the ets() function.

Note that we did not specify a smoothing factor. The ets() function calculates the optimal smoothing factor (alpha, shown via the summary() function (in bold below)), which in this case is .99, which means that model time series takes about 99% of the previous value to incorporate into the next time series prediction:

library(dplyr) 
>
> Attaching package: 'dplyr'
> The following objects are masked from 'package:stats':
>
> filter, lag
> The following objects are masked from 'package:base':
>
> intersect, setdiff, setequal, union
library(forecast...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image