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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

Modifying the model to include a trend component

Earlier, we added a linear trend line to the data. If we wanted to incorporate a linear trend into the forecast as well, we can substitute A for the second parameter (trend parameter), which yields an "AAN" model (Holt's linear trend). This type of method allows exponential smoothing with a trend:

fit <- ets(x, model = "AAN")
summary(fit)
> ETS(A,A,N)
>
> Call:
> ets(y = x, model = "AAN")
>
> Smoothing parameters:
> alpha = 0.0312
> beta = 0.0312
>
> Initial states:
> l = 0.1641
> b = -0.0021
>
> sigma: 0.0042
>
> AIC AICc BIC
> -108.5711 -104.1267 -106.0149
>
> Training set error measures:
> ME RMSE MAE MPE MAPE
> Training set -0.000290753 0.004157744 0.003574276 -0.2632899 2.40212
> MASE ACF1
> Training set 0.7709083 0.05003007

Plotting...

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