Model Performance Overview
Model Performance Metrics
Meta-Learner Model Weights
XGBoost28.0%
Transformer35.0%
AdaBoost15.0%
LSTM12.0%
Random Forest10.0%
Real-time Prediction Confidence
Training Status
XGBoost
75%ETA: 2h 15m
Transformer
100%ETA: Done
LSTM
0%ETA: 4h 30m
Feature Importance
Price Momentum89%
Volume Profile76%
Market Sentiment64%
Technical Indicators58%
News Sentiment42%
Model Health
Data Quality
94%
Prediction Drift
12.0%
Model Stability
87%
Latency
78%
Transformer Architecture Deep Dive
Architecture Specs
Model Dimension512
Attention Heads8
Encoder Layers6
Sequence Length1440
Dropout Rate0.1
Training Progress
Training Loss0.0847
Validation Loss0.0923
Learning Rate3e-4
Attention Analysis
1-min patterns34%
5-min momentum28%
15-min trends22%
Hourly cycles16%