HARN: Hierarchical Associative Resonance Network for Event-Driven Multi-Timeframe Forecasting

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Highly relevant for quantitative trading due to its focus on event-driven, multi-timeframe forecasting which aligns with live market data structures. The emphasis on causal consistency and avoiding look-ahead leakage is critical for robust backtesting and live deployment. However, the lack of rigorous statistical significance testing against strong baselines limits immediate practical confidence.

The architecture involves complex components including causal multi-scale encoders, gated associative memory with residual-driven writes, cross-level resonance, and asynchronous state updates. Implementing the event-driven protocol correctly requires careful handling of timestamp alignment and state masking.

The paper provides a detailed code-level audit, configuration inventories, and per-seed metrics. Source code and checkpoints are claimed to be available in a repository, though the public URL returned a 404 during revision. Raw vendor data cannot be redistributed, but a vendor-neutral preprocessing pipeline is provided to regenerate datasets from compatible feeds.