This module introduces learners to the fundamental principles of forecasting within the field of business analytics. It explains the purpose and scope of forecasting, explores different forecasting methods, and highlights common challenges businesses face when predicting future trends. Learners will also gain practical insights into simple forecasting approaches, transformations, and accuracy evaluation techniques, building a strong foundation for advanced forecasting models.

This module explores how regression techniques and decomposition methods can be applied to time series forecasting. Learners will gain an in-depth understanding of simple, multiple, and non-linear regression, the use of predictors and lagged variables, and the unique considerations of time series regression. The module also introduces decomposition approaches to separate time series into trend, seasonal, cyclical, and irregular components, helping learners build accurate and interpretable forecasting models in R.

This module focuses on advanced time series forecasting techniques, including exponential smoothing, ARIMA, and Seasonal ARIMA models. Learners will explore the theoretical foundations and practical applications of autoregressive and moving average models, understand the role of ACF and PACF in model selection, and learn how to handle seasonal and non-seasonal time series data. By mastering these advanced methods, learners will be able to build robust and accurate forecasting models in R that address both short-term fluctuations and long-term seasonal trends.