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demand-forecasting

Generate demand forecasts from historical data using statistical and machine learning models.

56|16|Updated Oct 18, 2025

Search Tags:#time series #predictive analytics #sales forecasting #demand forecasting #forecasting models #supply chain planning

Ready to run

npx skills add https://github.com/kishorkukreja/awesome-supply-chain --skill demand-forecasting-kishorkukreja

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: demand-forecasting
Source: https://github.com/kishorkukreja/awesome-supply-chain/tree/main/skills/demand-forecasting
Command: npx skills add https://github.com/kishorkukreja/awesome-supply-chain --skill demand-forecasting-kishorkukreja

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, statsmodels, pmdarima, prophet, scikit-learn, xgboost, tensorflow, keras, hierarchicalforecast, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of predicting future customer demand, enabling businesses to optimize inventory, production, and resource allocation.

Core Features & Use Cases

Demand Pattern Recognition: Identifies stable, trending, seasonal, and intermittent demand patterns.
Forecasting Methods: Implements statistical (Exponential Smoothing, ARIMA, Prophet) and machine learning (Random Forest, XGBoost, LSTM) techniques.
Accuracy Measurement: Provides key metrics like MAPE, RMSE, and Bias to evaluate forecast performance.
Use Case: A retail manager can use this Skill to forecast sales for the upcoming holiday season, ensuring sufficient stock levels without overstocking.

Quick Start

Use the demand-forecasting skill to build a forecast for the provided historical sales data.

Frequently Asked Questions about demand-forecasting

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I forecast sales demand for seasonal retail trends?▼

You can forecast seasonal demand by applying statistical methods like Prophet or Exponential Smoothing to historical sales data, identifying seasonal patterns to generate accurate future demand predictions.

What is the best way to predict intermittent demand patterns?▼

Intermittent demand forecasting is supported by analyzing sparse historical data using machine learning models like XGBoost and LSTM, which identify irregular demand patterns to optimize inventory and production planning.

Can I use ARIMA and XGBoost together for time series forecasting?▼

Yes, you can combine ARIMA and XGBoost for time series forecasting. The skill supports both statistical and machine learning models, allowing you to capture linear trends and complex non-linear relationships in demand data.

How do I measure demand forecast accuracy using MAPE and RMSE?▼

You measure demand forecast accuracy by calculating MAPE, RMSE, and Bias metrics, comparing predicted values against actual historical sales data to evaluate model performance and ensure reliable supply chain planning.

Does hierarchical forecasting support causal factors for supply chain planning?▼

Yes, hierarchical forecasting supports causal factors for supply chain planning. It integrates external variables into predictive models, allowing you to forecast demand across different hierarchy levels while accounting for causal influences.

What are the limitations of using LSTM for sales forecasting?▼

Limitations of using LSTM for sales forecasting include requiring large volumes of historical data to train effectively and increased computational complexity compared to simpler statistical models like Exponential Smoothing for stable demand patterns.