Time Series Forecasting with Machine Learning in R: From Foundation to Advanced Methods

By Toyin O. Oguntola, Joseph Solomon, Dorcas O. Folarin

Time Series Forecasting with Machine Learning in R bridges the gap between traditional statistical forecasting and cutting-edge machine learning methods. Written in a practical and applied style, this book empowers readers to build, evaluate, and deploy predictive models for time-dependent data using the powerful R ecosystem.
Starting from the foundations of time series theory, readers will learn how to visualize, decompose, and understand patterns such as trend and seasonality. The book introduces classic models like ARIMA and exponential smoothing, before diving into advanced machine learning and deep learning techniques - including random forests, gradient boosting, LSTM networks, and hybrid forecasting systems.
With hands-on R code examples, real-world datasets, and clear explanations, this book is perfect for students, researchers, and data professionals who want to master predictive analytics for business, economics, finance, healthcare, and environmental data.
By the end of the book, readers will be able to: Preprocess and explore time series data using Tidyverse and tsibble packages.Build statistical models such as ARIMA, ETS, and SARIMA.Apply machine learning algorithms for forecasting with caret, mlr3, and tidymodels.Evaluate forecast accuracy using industry-standard metrics.Combine multiple models for robust and interpretable predictions.Develop end-to-end forecasting pipelines in R for real-world applications.Whether you are a beginner in time series or an experienced data scientist seeking to expand your forecasting toolkit, this comprehensive guide provides both the theoretical background and the hands-on experience needed to make accurate, data-driven forecasts.

• Format: Paperback
• Language: English
• ISBN: B0FVX2WTN3
• ISBN13: 9798269521404
• Release Date: October 2025
• Publisher: Independently Published
• Length: 144 Pages
• Weight: 0.44 lbs.
• Dimensions: 0.3" x 6.0" x 9.0"