Time Series Analysis and Forecasting with R

Understanding how data changes over time is essential for predicting future trends in fields ranging from finance to supply chain management. This course provides a solid foundation in time series analysis, teaching you how to identify patterns, decompose data, and build reliable forecasting models. You will learn to navigate the complexities of temporal data, moving from basic definitions to practical forecasting techniques using R. What you'll learn: - Understand the fundamental concepts of time series data and temporal structures. - Learn to decompose time series into trend, seasonal, and irregular components. - Apply exponential smoothing techniques to handle various data patterns. - Practice building forecasting models to predict future values based on past observations. - Explore modern evaluation metrics to assess the accuracy of your predictions. - Understand how to handle missing values and outliers in time-based datasets. The course starts with essential terminology and data preparation techniques before progressing through decomposition methods and written exercises in R. It is designed for beginners and data enthusiasts who want to master the basics of temporal analysis through clear explanations and code-based practice. Start building your skills in temporal data analysis today.