Challenge
Design a prediction system that can accurately model time series data across multiple temporal scales while accounting for various cyclical patterns like holidays, business hours, and seasonal effects.
Solution
Implemented a multi-scale ensemble architecture where each temporal model (hourly, daily, weekly) learns specific patterns, combined through stacked regression with extensive feature engineering for temporal patterns.
Implementation
- Developed three temporal scales (hourly, daily, weekly) using XGBoost
- Created comprehensive feature engineering system for each scale
- Implemented memory-based feature calculation with rolling statistics
- Built stacked regression model for ensemble combination
- Integrated calendar effects (holidays, business hours)
- Developed real-time visualization and monitoring system
Simulation Analysis
Multi-Scale Prediction
Visualization of the TEN system combining predictions from hourly, daily, and weekly models with real-time performance metrics
Hourly Prediction
Accuracy: 97.2%Latency: 25msDaily Forecast
Accuracy: 94.8%Latency: 35msWeekly Trends
Accuracy: 92.5%Latency: 45msTechnical Architecture
TEN Architecture
Core components of the Temporal Ensemble Network
Prediction Process
Sequence diagram showing the prediction workflow
