This book provides a hands-on introduction to time series data analysis in R, covering topics such as managing and cleaning data, preparing for analysis, and modeling and forecasting. It includes pedagogical features like end-of-chapter exercises, problems, quizzes, and case studies, with additional resources available on the companion website. Ideal for undergraduate and graduate students and researchers in data-rich disciplines.

Format: Hardback
Length: 550 pages
Publication date: 11 May 2023
Publisher: Cambridge University Press

This comprehensive guide offers a user-friendly introduction to time series data analysis in R, covering essential topics such as managing and cleaning time series data of varying sizes, scales, and granularity, data preparation for analysis and visualization, and various approaches to classical and machine learning time series modeling and forecasting. With a focus on practical applications, the book includes end-of-chapter exercises, problems, quizzes, and case studies to support students' learning. The case studies are designed to challenge learners by introducing larger data sets, advanced data management skills, and R packages and functions suitable for real-world data analysis. In addition to providing commented R programs and data sets, the companion website offers additional case studies, lecture slides, videos, and exercise solutions. Accessible to those with a basic background in statistics and probability, this text serves as an ideal hands-on resource for undergraduate and graduate students, as well as researchers in data-rich disciplines.

Introduction to Time Series Data Analysis in R

Time series data analysis is a crucial field that involves the study and analysis of sequential data points collected over an extended period. It encompasses a wide range of applications, including finance, weather forecasting, healthcare, and natural sciences. In this guide, we will explore the intricacies of managing and cleaning time series data in R, a powerful programming language for statistical computing.

Managing and Cleaning Time Series Data

Time series data can come in various sizes, scales, and granularity, making it challenging to handle and analyze effectively. The first step in time series data analysis is to manage and clean the data to ensure its quality and suitability for analysis. This involves tasks such as identifying missing values, correcting outliers, and handling temporal dependencies.

Data Preparation for Analysis and Visualization

Once the data is cleaned and prepared, it is ready for analysis and visualization. This involves tasks such as transforming the data into a suitable format, selecting appropriate statistical models, and performing exploratory data analysis to identify patterns and trends.

Classical and Machine Learning Time Series Modeling and Forecasting

Time series data analysis offers a wide range of modeling and forecasting approaches, including classical time series models and machine learning algorithms. Classical time series models rely on statistical techniques such as autoregressive models, moving average models, and exponential smoothing models. Machine learning algorithms, on the other hand, use neural networks, random forests, and support vector machines to analyze and predict time series data.

Pedagogical Features

To support students' learning, this guide includes a range of pedagogical features. End-of-chapter exercises, problems, quizzes, and case studies are provided to reinforce the concepts covered in each chapter. These exercises and problems are designed to challenge students and help them apply the knowledge gained in the text.

Case Studies

Case studies are an essential component of time series data analysis. They provide real-world examples and applications of the concepts discussed in the text. The case studies in this guide are designed to stretch the learner, introducing larger data sets, enhanced data management skills, and R packages and functions appropriate for real-world data analysis.

Companion Website

In addition to the text, a companion website is available to provide additional resources and support for students. The website includes extra case studies, lecture slides, videos, and exercise solutions. This resource is designed to enhance the learning experience and provide students with additional opportunities to practice and apply the knowledge gained in the text.

Accessible to All

This guide is designed to be accessible to students with a basic background in statistics and probability. It assumes no prior knowledge of R or time series data analysis and provides a step-by-step approach to learning the subject. Whether you are an undergraduate or graduate student, or a researcher in data-rich disciplines, this guide will help you develop the skills and knowledge necessary to analyze and forecast time series data effectively.

In conclusion, this comprehensive guide offers a user-friendly introduction to time series data analysis in R. It covers essential topics such as managing and cleaning time series data, data preparation for analysis and visualization, classical and machine learning time series modeling and forecasting, and pedagogical features to support students' learning. With a focus on practical applications and real-world examples, this guide is an ideal hands-on text for undergraduate and graduate students, as well as researchers in data-rich disciplines. By mastering the techniques and concepts discussed in this guide, you will be well-equipped to analyze and forecast time series data effectively and contribute to your field of study.

Weight: 1008g
Dimension: 249 x 178 x 30 (mm)
ISBN-13: 9781108837774