Code
sample R code
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x+y[1] 8Business Forecasting with R
Welcome
Business Forecasting with R is a concise and practice-oriented guide designed to equip learners with the analytical skills required to predict future business trends using real-world data. This course blends foundational forecasting theory with hands-on implementation in R, making it ideal for students, analysts, and professionals across business domains.
Key Learning Components:
- Fundamentals of Business Forecasting: Understand the role of forecasting in strategic planning and decision-making. Learn about different types of forecasting methods and the nature of time series data in business.
- Time Series Analysis Using R: Explore how to visualize, transform, and decompose time series data into trend, seasonal, and irregular components using R’s powerful statistical capabilities.
- Classical Forecasting Techniques: Apply methods like moving averages, exponential smoothing, and least-squares trend fitting (linear, quadratic, and exponential) to build basic yet effective forecasting models.
- Advanced Modeling Approaches: Delve into ARIMA and SARIMA models for handling autocorrelation and seasonality. Understand the application of ARCH and GARCH models in measuring volatility in financial and economic data.
- Model Evaluation and Forecast Accuracy: Use performance metrics such as RMSE, MAPE, and AIC to compare forecasting models and enhance prediction reliability. Learn to build hybrid models for improved performance.
- Refined Forecasting and Adjustments: Implement hierarchical forecasting and incorporate external variables (e.g., promotions, market shocks, outliers) to improve the accuracy and contextual relevance of your forecasts.
By the end of the course, students will have the capability to build, assess, and apply robust forecasting models in R, enabling data-driven decision-making in various business functions such as finance, marketing, operations, and supply chain management.
Inclusion of codes in this book
Throughout the book, the codes used for the analysis are included in this document as shown below. And the output of each code is given below the code.
About the Authors
Vijayakumar P is an accomplished educator and data professional with over eight years of teaching and research experience in business analytics, data science, and HR analytics. He is a UGC Junior Research Fellow (JRF) and has qualified the UGC NET and SET in Management multiple times, reflecting his strong academic foundation.
Proficient in Python, R, STATA, SPSS, AMOS, PLS-SEM, Tableau, Power BI, Google Looker Studio, GitHub, and Excel , he has guided students and professionals in transforming data into actionable insights. His teaching spans business analytics using R and Python, HR metrics and dashboards, business forecasting, and business research methods, and he has also developed interactive eBooks to enhance hands-on learning.
Vijayakumar’s work is marked by clarity, precision, and innovation—whether in research, classroom teaching, or applied analytics—making data more accessible, impactful, and meaningful for decision-making.
Reference books
Statistics for Management (Levin, Rubin)