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Practical Predictive Analytics

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

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781785886188
Length 576 pages
Edition 1st Edition
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Author (1):
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Ralph Winters Ralph Winters
Author Profile Icon Ralph Winters
Ralph Winters
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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

Using the ets function

While moving averages re extremely useful, they are only one component of what is known as an exponential smoothed state space model, which has many options to define the optimal smoothing factor, as well as enabling you to define the type of trend and seasonality via the parameters.
To implement this model we will use the ets() function from the forecast package to model the Not-Covered Percent variable for the "ALL AGES" category.

The ets() function is flexible in that it can also incorporate trend, as well as seasonality for its forecasts.

We will just be illustrating a simple exponentially smoothed model (ANN). However, for completeness, you should know that you specify three letters when calling the ets() function, and you should be aware of what each letter represents. Otherwise, it will model based upon the default parameters.

Here is the...

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