Name: Demand Forecasting Engine
Author: a5c-ai

Overview

The Demand Forecasting Engine provides comprehensive statistical and machine learning-based demand forecasting capabilities. It supports multiple forecasting algorithms with automatic model selection, ensemble averaging, and continuous accuracy tracking to generate reliable demand predictions for supply chain planning.

Capabilities

Time Series Forecasting: ARIMA, exponential smoothing, Holt-Winters methods
Machine Learning Models: XGBoost, LSTM neural networks for complex demand patterns
Causal Factor Integration: Incorporate promotions, seasonality, trends, and external drivers
Demand Sensing: Short-term signal incorporation for near-term forecast adjustment
Accuracy Metrics: MAPE, WMAPE, bias calculation and tracking
Automatic Model Selection: Best-fit algorithm selection based on data characteristics
Ensemble Averaging: Combine multiple model outputs for improved accuracy
Confidence Intervals: Generate prediction intervals for uncertainty quantification

Demand Forecasting Engine

Demand Forecasting Engine

Overview

Capabilities

Input Schema

Output Schema

Usage

Generate SKU-Level Forecast

Promotional Demand Planning

Multi-Model Ensemble

Integration Points

Process Dependencies

Best Practices

Continuous Learning V2

Continuous Learning V2

Continuous Learning V2

Continuous Learning

Continuous Learning

Pytorch Patterns