- Learn more about the other measures of accuracy provided by
forecast
function'saccuracy
function. What advantages does MAPE have over RMSE/MAE - Learn how to interpret the alpha, beta, gamma, (and sometimes phi) parameters of an ETS object. What does it mean to have a low beta parameter? Try to explicitly specify extreme values for some of these parameters using named arguments in the
ets
function. How does it affect the quality and dynamics of your forecasts? - The GitHub repository that we used to download some of the data sets in this chapter (and we will be using for different data sets for subsequent chapters, as well) also contains a data set containing Google searches for Dr. Martin Luther King, Jr., what example series in this chapter does this most resemble? Is there seasonality in this series? If so, when do the spikes in searches occur? Why do you think that is? Take your best guess as to which particular ETS model
ets
would chose to describe this series. Try it for yourself...
Data Analysis with R, Second Edition - Second Edition
Data Analysis with R, Second Edition - Second Edition
Overview of this book
Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly.
Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples.
Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility.
This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst.
Table of Contents (24 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Free Chapter
RefresheR
The Shape of Data
Describing Relationships
Probability
Using Data To Reason About The World
Testing Hypotheses
Bayesian Methods
The Bootstrap
Predicting Continuous Variables
Predicting Categorical Variables
Predicting Changes with Time
Sources of Data
Dealing with Missing Data
Dealing with Messy Data
Dealing with Large Data
Working with Popular R Packages
Reproducibility and Best Practices
Other Books You May Enjoy
Index
Customer Reviews