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

HARN：用于事件驱动多时间框架预测的分层关联共振网络

Abstract: Financial time series evolve across multiple temporal resolutions, challenging forecasting systems to incorporate newly available information without repeatedly recomputing unchanged representations. 摘要： 金融时间序列在多个时间分辨率上演变，这对预测系统提出了挑战，即如何在不重复计算未变表示的情况下整合新获得的信息。

We introduce HARN, a Hierarchical Associative Resonance Network for event-driven multi-timeframe forecasting. HARN maintains persistent representations across temporal levels and updates each level only when its corresponding completed bar becomes available. 我们引入了 HARN，这是一种用于事件驱动多时间框架预测的分层关联共振网络（Hierarchical Associative Resonance Network）。HARN 在各个时间层级上保持持久的表示，并且仅在相应的完整数据柱（bar）可用时才更新每个层级。

The architecture combines causal multi-scale temporal encoding, gated associative memory, cross-level resonance, and hierarchical evidence aggregation, with forecasting performed in basis-point space and reconstructed to the original price scale. 该架构结合了因果多尺度时间编码、门控关联记忆、跨层级共振以及分层证据聚合，预测在基点空间中执行，并重构回原始价格尺度。

We evaluate HARN on four assets spanning equity, foreign exchange, and commodity markets using multiple random seeds and component ablations. HARN achieves competitive reconstructed-price forecasting errors against single-timeframe PatchTST and TimeXer baselines, while ablations reveal the effects of removing individual components across assets and timeframes. 我们使用多个随机种子和组件消融实验，在涵盖股票、外汇和商品市场的四种资产上对 HARN 进行了评估。HARN 在重构价格预测误差方面取得了与单时间框架 PatchTST 和 TimeXer 基准相当的竞争性表现，同时消融实验揭示了在不同资产和时间框架下移除单个组件的影响。

A code-level audit further examines consistency between the implementation and the defined event-driven causal protocol. The results position HARN as a persistent multi-timeframe forecasting framework rather than evidence of universal predictive superiority. 代码级审计进一步检查了实现与定义的事件驱动因果协议之间的一致性。研究结果将 HARN 定位为一个持久的多时间框架预测框架，而非通用预测优势的证明。