Retail Sales Forecasting and Seasonal Trend Analysis using SARIMA: A Time Series Analysis from 2020–2025

A complete, reproducible dataset and workflow are provided for retail sales forecasting and seasonal trend analysis using SARIMA (Seasonal AutoRegressive Integrated Moving Average) models implemented via visual programming in KNIME. The package includes: 1. Excel dataset containing historical retail sales data (2020–2025) with columns: Date, Month, Year, Sales, Promo_Spent, Customer_Count, Avg_Transaction_Value, along with sample indicators for seasonality and promotional effects. 2. Workflow image visualizing the step-by-step pipeline used to process the data, perform seasonal decomposition, fit SARIMA models, and generate forecasts. 3. LaTeX source file for the accompanying research paper, detailing methodology, model formulation, evaluation metrics (MAE, RMSE, MAPE), and theoretical foundations. 4. PDF version of the research paper for easy reading and reference. The workflow is designed to be fully reproducible, modular, and interpretable, allowing researchers and data practitioners to apply the methods to their own time series datasets. It demonstrates the practical integration of statistical modeling with visual programming tools for efficient, transparent, and accessible data analytics. This submission is intended to support both educational purposes and professional research in predictive analytics and time series modeling.