Forecasting with R Training in Copenhagen, Denmark

This course provides a hands-on approach to time series forecasting using R. Participants will explore key forecasting techniques such as exponential smoothing, ARIMA, and machine learning models within the R ecosystem. The course focuses on practical application, enabling professionals to model, analyze, and forecast data trends effectively for strategic decision-making.

Course Agenda:

Introduction to Forecasting and R Tools

• Principles of Forecasting

• Time Series Data Structures in R (ts, tsibble)

• Data Preprocessing and Visualization

• Introduction to the forecast and fable packages

Classical Forecasting Techniques

• Time Series Decomposition (Trend, Seasonality, Residuals)

• Simple and Exponential Smoothing Methods

• ARIMA and Seasonal ARIMA (SARIMA) Modeling

• Stationarity, Differencing, and ACF/PACF Plots

Advanced Methods and Applications

• Forecast Accuracy Metrics (MAE, RMSE, MAPE)

• Forecasting with Regressors (ARIMAX, Dynamic Regression)

• Machine Learning Methods for Forecasting (Random Forests, XGBoost)

• Automating Forecasting Pipelines

• Case Studies and Evaluation