Master Time Series Forecasting with R: Analyze & Predict

Overview

By the end of this course, learners will be able to define the fundamentals of forecasting, classify forecasting methods, apply regression and decomposition techniques, and implement advanced models like ARIMA and SARIMA to accurately predict time-dependent data. This comprehensive course equips participants with the tools to tackle real-world forecasting challenges using R. Beginning with the foundations of business analytics forecasting, learners will explore methods, steps, and common pitfalls before moving into practical applications of simple forecasting models. The course then advances into regression-based forecasting, covering simple, multiple, and non-linear regression, while also integrating predictors and lagged variables for more reliable time series analysis. Finally, learners will gain hands-on expertise with exponential smoothing, ARIMA, and Seasonal ARIMA modeling, supported by ACF and PACF diagnostics. What makes this course unique is its step-by-step progression from basics to advanced forecasting, its practical use of R for implementation, and its focus on both interpretability and accuracy. By completing this program, learners will be prepared to design robust forecasting solutions that improve decision-making in business, finance, operations, and beyond.

Syllabus

Foundations of Forecasting

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.

Regression and Decomposition in Time Series

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.

Advanced Forecasting Models

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.