Forecasting System Documentation

Welcome to the documentation for the Forecasting System.

This project provides a scalable and automated time series forecasting pipeline designed for real-time and batch use cases, with model selection, data clustering, and hierarchical reconciliation.

Use the sidebar to explore documentation sections including models used, pipeline structure, deployment workflow, and technical appendices.

Project Overview

This project aims to develop a sales forecasting system, a large-scale retailer with numerous stores distributed across multiple geographic regions. The forecasting model is designed to capture the unique characteristics of each store and product group - across five main product categories: Fresh Food, Packaged Food, Home & Personal Care, Household Items, and Apparel - as well as regional differences across locations.

The entire pipeline is fully automated following the standard machine learning lifecycle, including data ingestion, preprocessing, feature engineering, model training and evaluation and deployment. Forecasts are generated in both batch and real-time formats. Time series data is thoroughly analyzed using features such as trend, seasonality and volatility, followed by KMeans clustering to optimize forecast accuracy for different store and product group profiles.

The system integrates several tools to ensure reliability, reusability and scalability: DVC for data version control, MLflow for model lifecycle management, FastAPI for serving prediction APIs, Airflow for orchestration of data processing workflows, Slack notifications for real-time monitoring and alerts and a Data Pipeline framework to manage end-to-end processing.

This solution enables the business team to better plan sales, allocate resources more efficiently, and make data-driven decisions quickly, by leveraging both historical data and future projections.