Modern Time Series Analysis with R provides 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 book then guides 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 for 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.

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Spis treści

Modern Time Series Analysis with R. Practical forecasting and impact estimation with tidy, reproducible workflows eBook -- spis treści

• 1. R, RStudio, and R packages
• 2. Objects and Functions in R
• 3. Data Input/Output in R
• 4. Time Series Characteristics
• 5. Time Series Data Wrangling and Visualization
• 6. Business Applications of Time Series Analysis
• 7. Time Series Adjustments, Transformations, and Decomposition
• 8. Time Series Features
• 9. Time Series Smoothing and Filtering
• 10. Basics of Forecasting
• 11. Exponential Smoothing
• 12. ARIMA Forecasting Models
• 13. Advanced Computational Methods for Forecasting
• 14. Forecasting Models for Multiple Time Series
• 15. Causal Impact Estimation
• 16. Changepoint Detection
• 17. Anomaly Detection and Imputation