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Hands-On Ensemble Learning with R

You're reading from  Hands-On Ensemble Learning with R

Product type Book
Published in Jul 2018
Publisher Packt
ISBN-13 9781788624145
Pages 376 pages
Edition 1st Edition
Languages
Author (1):
Prabhanjan Narayanachar Tattar Prabhanjan Narayanachar Tattar
Profile icon Prabhanjan Narayanachar Tattar
Toc

Table of Contents (17) Chapters close

Hands-On Ensemble Learning with R
Contributors
Preface
1. Introduction to Ensemble Techniques 2. Bootstrapping 3. Bagging 4. Random Forests 5. The Bare Bones Boosting Algorithms 6. Boosting Refinements 7. The General Ensemble Technique 8. Ensemble Diagnostics 9. Ensembling Regression Models 10. Ensembling Survival Models 11. Ensembling Time Series Models 12. What's Next?
Bibliography Index

Time series visualization


The main characteristic of time series data is that the observations are taken at regular intervals. A plot of the time series values (the y axis) against the time itself (x axis) is of great importance and gives away many structural insights. A time series plot is not merely a scatterplot with time as the x axis. The time is non-decreasing and hence it has more importance and meaning in a time series plot than the mere x axis in a scatterplot. For instance, lines can connect the points of a time series plot that will indicate the path of the time series, and such a connection would be meaningless in the scatterplot, which would be all over the place. The path will generally indicate the trend and as such, shows in which direction the series will go next. Changes in time series are easily depicted in the plot. We will now visualize the different time series.

The plot.ts function is central to the scheme of visualization here. An external graphical device of appropriate...

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