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Full Description
Gain expertise in modern time series forecasting and causal inference in R to solve real-world business problems with reproducible, high-quality code
Key Features
Explore forecasting and causal inference with practical R examples
Build reproducible, high-quality time series workflows using tidyverse and modern R packages
Apply models to real-world business scenarios with step-by-step guidance
Purchase of the print or Kindle book includes a free PDF eBook
Book DescriptionModern Time Series Analysis with R is a comprehensive, hands-on guide to mastering the art of time series analysis using the R programming language. Written by leading experts in applied statistics and econometrics, this book helps data scientists, analysts, and developers bridge the gap between traditional statistical theory and practical business applications.
Starting with the foundations of R and tidyverse, you'll explore the core components of time series data, data wrangling, and visualization techniques. The chapters then guide you through key modeling approaches, ranging from classical methods like ARIMA and exponential smoothing to advanced computational techniques, such as machine learning, deep learning, and ensemble forecasting.
Beyond forecasting, you'll discover how time series can be applied to causal inference, anomaly detection, change point analysis, and multiple time series modeling. Practical examples and reproducible code will empower you to assess business problems, choose optimal solutions, and communicate results effectively through dynamic R-based reporting.
By the end of this book, you'll be confident in applying modern time series methods to real-world data, delivering actionable insights for strategic decision-making in business, finance, technology, and beyond.What you will learn
Understand the core concepts and structure of time series data
Wrangle and visualize time series effectively
Apply transformations and decomposition techniques
Build and compare univariate forecasting models
Apply statistical, ML, and DL models strategically based on context
Forecast hierarchical and grouped time series
Measure causal impact using interrupted time series analysis
Detect anomalies, structural changes, and handle missing data
Who this book is forThis book is for data scientists, analysts, and developers who want to master time series analysis using R. It is ideal for professionals in finance, retail, technology, and research, as well as students seeking practical, business-oriented approaches to forecasting and causal inference. Basic knowledge of R is assumed, but no advanced mathematics is required.
Contents
Table of Contents
R, RStudio, and R packages
Objects and Functions in R
Data Input/Output in R
Time Series Characteristics
Time Series Data Wrangling and Visualization
Business Applications of Time Series Analysis
Time Series Adjustments, Transformations, and Decomposition
Time Series Features
Time Series Smoothing and Filtering
Basics of Forecasting
Exponential Smoothing
ARIMA Forecasting Models
Advanced Computational Methods for Forecasting
Forecasting Models for Multiple Time Series
Causal Impact Estimation
Changepoint Detection
Anomaly Detection and Imputation
