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 with R. Practical forecasting and causal inference with tidy, reproducible workflows eBook -- spis treści

• 1. R, RStudio and R packages
• 2. Writing functions in R
• 3. Loading data into R workspace
• 4. Time series Characteristics
• 5. Time Series Data Wrangling
• 6. Time Series Visualisation
• 7. Time Series Problem Spaces
• 8. Time Series Decomposition
• 9. Time Series Smoothing
• 10. Seasonality Analysis
• 11. Time Series Features
• 12. Forecasting models for univariate time series
• 13. Bayesian forecasting models
• 14. Machine Learning Forecasting Methods
• 15. Deep Learning forecasting Models
• 16. Model Evaluation and Measure Forecast Accuracy
• 17. Anomaly Detection and Imputation