Multivariate Time Series Analysis and Forecasting in R

In many industries, data points rarely exist in isolation; they influence one another over time. Understanding how multiple variables interact is essential for accurate forecasting and strategic planning. This course takes you from the fundamentals of time series data to building robust multivariate models. You will learn to identify patterns across multiple datasets and apply statistical techniques to predict future trends with precision. What you'll learn: - Understand the core principles of multivariate time series and their role in modern business analytics - Identify dependencies and lead-lag relationships between different data variables - Apply vector autoregression and other modeling techniques within the R environment - Practice data cleaning and transformation specifically for time-indexed multivariate datasets - Evaluate model performance using modern error metrics and diagnostic checks - Implement R workflows using tidy data structures for efficient time series management The course begins with foundational definitions and data structures before moving into model selection, estimation, and practical forecasting exercises. It is designed for beginners in data science and analysts who have a basic grasp of R and want to expand their forecasting toolkit. Start building more accurate and insightful predictive models today.