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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

Plotting future values with confidence bands

Use the plot function to plot future predictions. Notice that the prediction for the last value encompasses upper and lower confidence bands surrounding a horizontal prediction line. But why a horizontal prediction line? This is saying that there is no trend or seasonality for the exponential model, and that the best prediction is based upon the last value of the smoothed average. However, we can see that there is significant variation to the prediction, based upon the confidence bands. The confidence bands will also increase in size as the forecast period increases, to reflect the uncertainty associated with the forecast:

plot(forecast(fit))

 

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