HARN：用于事件驱动多时间框架预测的层次关联共振网络
HARN: Hierarchical Associative Resonance Network for Event-Driven Multi-Timeframe Forecasting
- Dr. APJ Abdul Kalam Technical University（APJ阿卜杜勒·卡拉姆技术大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
HARN是一种层次关联共振网络，通过持久表示和事件驱动更新实现多时间框架预测，在四个资产上达到与基线相当的误差，并验证了组件有效性。
AI中文摘要：
金融时间序列在多个时间分辨率上演变，这给预测系统带来了挑战，即如何在不重复计算未变化表示的情况下整合新获得的信息。我们引入了HARN，一种用于事件驱动多时间框架预测的层次关联共振网络。HARN在时间层级间维持持久表示，并且仅当相应的已完成柱状图可用时才更新每个层级。该架构结合了因果多尺度时间编码、门控联想记忆、跨层级共振以及层次证据聚合，预测在基点空间中进行，并重构回原始价格尺度。我们在涵盖股票、外汇和大宗商品市场的四个资产上评估了HARN，使用了多个随机种子和组件消融实验。HARN在重构价格预测误差上与单时间框架的PatchTST和TimeXer基线相比具有竞争力，而消融实验揭示了在不同资产和时间框架上移除各个组件的影响。一项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。结果将HARN定位为一个持久的多时间框架预测框架，而非普遍预测优越性的证据。
英文摘要：
Financial time series evolve across multiple temporal resolutions, challenging forecasting systems to incorporate newly available information without repeatedly recomputing unchanged representations. 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. 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. 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. 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.
