Seasonality Detection & Pattern Recognition
Multi-scale temporal pattern identification across daily, weekly, and monthly cycles
Real-time energy price patterns showing daily peaks, weekly cycles, and seasonal trends
STL Decomposition
Seasonal-Trend decomposition captures evolving seasonal patterns with 0.92 R² accuracy
Fourier Analysis
FFT identifies 24h, 168h, and 8760h dominant frequencies in energy pricing
Wavelet Transform
Multi-resolution analysis detects irregular seasonal changes and price volatility
External Factor Integration
Weather data, currency rates, and renewable generation forecasts
Weather Data
Temperature: 0.89 correlation
Wind Speed: Multi-height sampling
Solar Irradiance: 950 W/m² forecast
Currency Impact
EUR/USD: Cross-border trading
VECM Models: Cointegration analysis
Lag Structure: 1-48 hour optimization
Market Fundamentals
Supply/Demand: Grid balance
Outages: Plant availability
Transmission: Congestion pricing
Attention Mechanism
Dynamic Weighting: Factor importance
Temporal Patterns: Time-varying impact
Feature Selection: Automated relevance
ARIMA vs LSTM Performance Analysis
Comprehensive comparison across accuracy, interpretability, and computational requirements
ARIMA Models
Accuracy
87.3%
RMSE
49.90
Training Time
2.3 min
Best for: Linear patterns, interpretability, confidence intervals
LSTM Networks
Accuracy
94.7%
RMSE
18.74
Training Time
45.7 min
Best for: Non-linear patterns, multivariate inputs, maximum accuracy
Hybrid Model
Accuracy
96.2%
RMSE
7.35
Training Time
52.1 min
Best for: Production systems, optimal accuracy, robust performance
95% Confidence Thresholds for Automated Decisions
Statistical rigor for risk-aware algorithmic trading
95% confidence intervals (blue band) with prediction line (orange) for automated trading decisions
Entry Threshold
2.0σ confidence level triggers position entry with dynamic volatility adjustment
Risk Management
VaR constraints and circuit breakers at 5× historical volatility
Position Sizing
Kelly criterion with uncertainty discounts based on interval width
Trading Decision Framework
Entry Signal
±2.0σ
95% confidence threshold
Confirmation
±2.5σ
99% confidence level
Stop Loss
-1.5σ
Risk protection
Profit Target
+1.0σ
Conservative exit
Model Performance Benchmarks
- LSTM Architecture: 94.7% accuracy on energy price forecasting
- SARIMA Model: RMSE 49.90, MAPE 12.05% for linear patterns
- Hybrid Model: 13.08% MAE improvement over individual models
- Processing Latency: <100ms for real-time inference
- Training Dataset: 3-year historical energy market data
- Validation Method: Walk-forward analysis with expanding windows
Technical Implementation Stack
- Deep Learning: TensorFlow/Keras with LSTM architectures
- Classical Models: Statsmodels SARIMA implementation
- Data Pipeline: Apache Kafka + InfluxDB time-series storage
- Deployment: Kubernetes with auto-scaling capabilities
- Monitoring: Prometheus + Grafana with custom forecasting metrics
- Compliance: Automated model validation and regulatory reporting