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 Description Modern 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 core concepts and components of time series data - Wrangle and visualize time series with tidyverse and R packages - Apply ARIMA, exponential smoothing, and machine learning methods - Explore deep learning and ensemble forecasting approaches - Conduct causal inference with interrupted time series analysis - Detect anomalies, structural changes, and perform change point analysis - Analyze multiple time series using hierarchical and grouped models - Automate reproducible reporting with RStudio and dynamic documents Who this book is for This 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. 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 “This guide bridges the gap between raw data and strategic foresight using R as incremental learning. It excels in its treatment of causality, moving beyond forecasting to explain data shifts through rigorous impact estimation. It is a strong resource for professionals seeking to master temporal dynamics while appreciating R.” Alain Briançon, PhD. Senior Technology Executive, Strategic Innovator and VP of Research and Data Science, Dynata “An exceptional bridge between rigorous theory and modern R workflows. It addresses every critical phase – from forecasting to causal impact measurement – with remarkably practical, tidy-centric code. An essential toolkit for professionals in both academia and industry.” Dr. Baki Billah, Associate Professor of Biostatistics, Monash University “One of the perennial challenges with R is that its richness and complexity can make it hard to find an on-ramp to the specific area you need to master. If that area is time series, this book is your roadmap, taking you from zero to hero without assuming prior R experience. It offers a pragmatic, fully functional slice of the R universe for the time series practitioner who just needs to get started.” Kendra Vant, MIT PhD, AI Product Leader and Founder, Europa Labs Dr. Yeasmin Khandakar is a data scientist with over 15 years of experience across diverse sectors, including FinTech (Portland House Group), MedTech (Optalert), retail (Coles, Officeworks) and transport (Transurban). She has a PhD from Monash University and is the co-author of the paper Automatic time series forecasting: the forecast package for R, which has generated over 5,900+ citations. Dr. Khandakar