Overview

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Master the principles and practice of time series forecasting with R and build the skills to analyze historical data and generate reliable predictions. This course takes you through a structured learning journey, beginning with the fundamentals of forecasting in business analytics before progressing to regression, decomposition, and advanced forecasting models. You will learn how to distinguish between qualitative and quantitative forecasting methods, apply simple forecasting techniques, evaluate forecast accuracy, and address common forecasting challenges. As you advance, you will use simple, multiple, and non-linear regression, incorporate predictor and lagged variables, and decompose time series into trend, seasonal, cyclical, and irregular components to create more interpretable forecasting models. The course concludes with advanced techniques, including exponential smoothing, ARIMA, and Seasonal ARIMA (SARIMA), while using ACF and PACF diagnostics to support effective model selection and implementation in R. Designed for learners who want to strengthen their forecasting capabilities, this course combines foundational concepts with practical implementation in R through a logical, step-by-step progression. By the end of the course, you will be able to select appropriate forecasting methods, build and evaluate time series forecasting models, and develop accurate forecasting solutions that support data-driven decision-making across a wide range of business applications.

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.