MS-CPAT-LOB v4 Regime-Aware Hybrid Architecture
About This Architecture
MS-CPAT-LOB v4 combines multi-scale CNNs, state-space models, and efficient transformers to forecast limit order book dynamics with regime awareness. Raw LOB tensors flow through feature engineering, multi-scale convolutions (1×1, 3×3, 5×5), SSM selective scan compression, and sparse attention fusion, with a regime classifier routing through mixture-of-experts layers. This hybrid architecture reduces computational complexity to O(n log n) while capturing both local patterns and long-range temporal dependencies in high-frequency market data. Fork and customize this diagram on Diagrams.so to adapt the fusion strategy, attention mechanism, or regime routing for your own financial ML pipeline.
People also ask
How can I build a hybrid deep learning model that combines CNNs, state-space models, and transformers for limit order book forecasting with regime awareness?
MS-CPAT-LOB v4 demonstrates a complete pipeline: LOB tensors enter feature engineering (order book imbalance, bid-ask spread, microprice), flow through multi-scale convolutions for local patterns, compress via SSM selective scan (O(n) complexity), fuse with efficient sparse attention, and route through regime-conditioned mixture-of-experts before dual output heads for forecasting and classificatio
- Domain:
- Ml Pipeline
- Audience:
- Machine learning engineers building financial forecasting models with hybrid deep learning architectures
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