Retail — Sales Forecast Feature Engineering Pipeline
FreeThis DAG constructs a feature engineering pipeline for sales forecasting models, integrating historical data and performance indicators. It ensures high-quality features through data transformation and validation techniques.
Overview
The Sales Forecast Feature Engineering Pipeline is designed to enhance the accuracy of sales forecasting models within the retail industry. By leveraging historical sales data, performance metrics, and key indicators, this pipeline systematically ingests and processes data to create robust features for machine learning applications. The architecture consists of several stages, starting with the ingestion of data from diverse sources such as transaction logs, customer behavior analytics, and inve
The Sales Forecast Feature Engineering Pipeline is designed to enhance the accuracy of sales forecasting models within the retail industry. By leveraging historical sales data, performance metrics, and key indicators, this pipeline systematically ingests and processes data to create robust features for machine learning applications. The architecture consists of several stages, starting with the ingestion of data from diverse sources such as transaction logs, customer behavior analytics, and inventory levels. The pipeline employs data transformation techniques, including normalization, aggregation, and feature extraction, to convert raw data into meaningful inputs for model training. Quality control measures are implemented at each step to validate the integrity and relevance of the features, ensuring that only high-quality data is used. The outputs of this pipeline include a comprehensive feature set ready for model training, performance reports, and dashboards for monitoring key performance indicators (KPIs) such as forecast accuracy and model performance metrics. By providing a structured approach to feature engineering, this pipeline significantly enhances the predictive capabilities of sales models, ultimately driving better business decisions and improved inventory management in the retail sector.
Part of the Data & Model Catalog solution for the Retail industry.
Use cases
- Improves forecasting accuracy, reducing stockouts and overstock
- Enhances decision-making with data-driven insights
- Optimizes inventory management and resource allocation
- Increases sales through better demand prediction
- Supports agile responses to market changes and trends
Technical Specifications
Inputs
- • Historical sales transaction logs
- • Customer behavior analytics data
- • Inventory level records
- • Promotional campaign performance data
- • Market trend reports
Outputs
- • Feature set for sales forecasting models
- • Performance evaluation reports
- • Dashboards for KPI monitoring
Processing Steps
- 1. Ingest historical sales and performance data
- 2. Transform data through normalization and aggregation
- 3. Extract features relevant to sales forecasting
- 4. Validate feature quality and relevance
- 5. Generate performance metrics for model training
- 6. Output feature set and performance reports
Additional Information
DAG ID
WK-0341
Last Updated
2025-02-18
Downloads
108