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 the art of extracting meaningful insights from, and revealing patterns in, time series data using statistical and data visualization approaches. These insights and patterns can then be utilized to explore past events and forecast future values in the series.

This book covers the following exciting features:

• Visualize time series data and derive better insights
• Explore auto-correlation and master statistical techniques
• Use time series analysis tools from the stats, TSstudio, and forecast packages
• Explore and identify seasonal and correlation patterns
• Work with different time series formats in R
• Explore time series models such as ARIMA, Holt-Winters, and more
• Evaluate high-performance forecasting solutions

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).

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

• R Machine Learning Projects [Packt] [Amazon]

• Machine Learning with R - Third Edition [Packt] [Amazon]

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.

Click here if you have any feedback or suggestions.