Hands-On Time Series Analysis with R
Build accurate forecasting models using ARIMA, Prophet, TBATS, Bayesian methods, and deep learning
SMCreated by Diogo Alves de Resende, Shuen Mei
Gain practical experience analyzing and forecasting time series data with R. Explore a range of methods from foundational techniques to advanced tools like neural networks and Prophet. Work with real-world examples to build skills that apply directly to business and research challenges.
Packt | Sep 2026 | 402 min
What You Will Learn
You will start by working with R fundamentals and time series data structures, then move on to visualization and decomposition. Step by step, you will build and evaluate forecasting models using both classical and modern approaches. Real-world case studies and hands-on exercises help reinforce each concept as you progress.
Key Features
- Apply ARIMA, Prophet, TBATS, and LSTM models to forecast time series data
- Visualize and interpret trends, seasonality, and anomalies in real datasets
- Fine-tune models for accuracy and adapt them to various business scenarios
Target Audience
Ideal for professionals in business intelligence, finance, or supply chain roles, as well as students and academics interested in data science. If you have a basic understanding of R and want to develop practical forecasting skills, you will find the content approachable and directly applicable to your work.
