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Demand Forecaster

Employing XGBoost and Prophet ML models, this agent analyzes historical request patterns, correlates with external factors (day of week, season, weather, events), and generates hourly/daily demand forecasts. It identifies potential capacity gaps and recommends preemptive worker scheduling.

Agent ID
demand-forecaster
Sector Workforce Management & Temporary Staffing
Status
Operational

Problem Statement

The challenge addressed

Reactive staffing leads to unfilled shifts and emergency premium costs. Proactive planning requires predicting demand patterns based on historical data, seasonality, and external factors.

Core Logic

How the agent solves it

Employing XGBoost and Prophet ML models, this agent analyzes historical request patterns, correlates with external factors (day of week, season, weather, events), and generates hourly/daily demand for...

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