Retail Sales Forecasting and Seasonal Trend Analysis using SARIMA

Retail Sales Database Integration & Secure Credential Access

Seasonality Pattern of Monthly Retail Sales (2020-2025)

Data Splitting

Train-Test Split Ratio: 83% / 17% (60 vs 12 rows)

• Training: 60 Monthly observations (2020-2024)

• Testing: 12 Monthly observations (2025)

SARIMA FORECASTING

(Predict 12-Month Horizon for 2025)

Generate sales forecast for 2025 using SARIMA model from previous step

Retail Sales Forecasting and Seasonal Trend Analysis using SARIMA

This Workflow aims to analyze and forecast monthly retails sales trends (2020-2025) using Time Series Analysis. The goal is to build a data-driven forecasting model capable of accurately predicting future sales while capturing both trend and seasonal patterns. Forecasting Retail sales helps optimize inventory management, financial planning, and strategic marketing decisions. By Leveraging SARIMA workflow in KNIME, this workflow demonstrates a fully automated, reproducible, and interpretable time series forecasting pipeline - without requiring a direct coding.

Compute Forecast Errors

Absolute Difference and % Deviation

Evaluate Model Accuracy

Mean Absolute Error and Mean Absolute Percentage Error

Evaluation Table and Visualization

Final Forecast Table and Line plot