Time Series Analysis and Forecasting with R

Organizations rely on accurate predictions to make informed decisions, yet time-dependent data requires specific analytical techniques to handle trends and seasonality. This course guides you through the process of cleaning, analyzing, and forecasting time series data using R, transforming raw historical numbers into actionable insights. What you'll learn: - Understand the fundamental principles of time series components like trend, seasonality, and cyclic patterns - Apply decomposition techniques to isolate underlying patterns in your data - Implement linear regression and exponential smoothing models for baseline forecasts - Configure advanced modeling techniques including ARIMA and SARIMA for complex datasets - Practice modern tidy forecasting workflows using contemporary R libraries for time-series data - Evaluate model accuracy using standard error metrics to ensure reliable predictions The curriculum begins with core definitions and statistical foundations before moving into the practical implementation of various forecasting methods. You will read through detailed explanations of model selection and parameter tuning to build robust predictive frameworks. This course is designed for beginners in data analysis or business analytics who want to specialize in forecasting; no prior experience with time series is required. Start building your forecasting skills today.