Book Image

Data Analysis with R, Second Edition - Second Edition

Book Image

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
Index

What is a time series?


Broadly, a time series is a set of data points, for one or more variables, that is put in the order of the time they occurred. When we speak of time series in this chapter, we are referring specifically to regularly spaced, fixed interval time series. As such, the measurements can be taken yearly, every second, and everything in between and beyond, as long as there is an equal interval of time between each successive observation and measurements exist at the end of every interval.

For the purposes of statistical time series forecasting, we treat observations as realizations of random variables, much like in virtually everything we've done so far. Pointedly, observations of a time series are realizations of astochastic process. Because our observations are recorded in intervals, as opposed to continuously, you can refer to time series data as adiscrete-timestochastic process,to impress a date (update: don't do this, it will backfire)!