This is the code repository for Hands-On Time Series Analysis with R, published by Packt.
Perform time series analysis and forecasting using R
Time series analysis is one of the key fields in statistical programming and it comprises of various techniques to analyze data to extract meaningful insights and other valuable characteristics from data. This book will be introducing readers to some powerful methods such as prediction and forecasting with Time Series Analysis using R. The book will equip you with tools and techniques which will let you confidently think through the problem.
This book covers the following exciting features:
- The practical & easy to follow codes to evaluate the high-performance forecasting solution.
- Develop a basic understanding of visualizing time series data in order to derive better insights.
- Explore auto-correlation and gain knowledge of statistical techniques to deal with non-stationary time series.
- Learn to build a Bayesian Structural Time Series model with external variables.
- Discover how to use time series analysis tools from the stats, forecast and astsa packages.
- Understand how to work with different time series formats in R (“ts”, “mts”, “xts” and “zoo” objects)
- Get introduced to traditional time series models like; ARIMA, Holt-Winters, ETS, etc.
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
library(TSstudio)
data(USgas)
Following is what you need for this book:
This book was written under the assumption that its readers have the following knowledge and skills:
- Basic knowledge of statistics or econometrics, which includes topics such as regression modeling, hypothesis testing, normal distribution, and so on
- Experience with R, or another programming language
With the following software and hardware list you can run all code files present in the book (Chapter 1-11).
| Chapter | Software required | OS required |
|---|---|---|
| 1-12 | R (≥ 3.0.2), Recommended R(≥ 3.4.0) | Windows, Mac OS X, and Linux (Any) |
Rami Krispin Rami Krispin is a data scientist at a major Silicon Valley company, where he focuses on time series analysis and forecasting. In his free time, he also develops open source tools and is the author of several R packages, including the TSstudio package for time series analysis and forecasting applications. Rami holds an MA in applied economics and an MS in actuarial mathematics from the University of Michigan—Ann Arbor.
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