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arXiv Machine Learning By Nabeel Ahmad Saidd

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

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HARN (Hierarchical Associative Resonance Network) is a new event‑driven framework for forecasting financial time series across multiple temporal resolutions. It preserves persistent representations at each temporal level, updating only when a new bar completes, and integrates causal multi‑scale encoding, gated associative memory, cross‑level resonance, and hierarchical evidence aggregation. Experiments on equity, FX, and commodity assets show that HARN matches the forecasting accuracy of single‑timeframe baselines while revealing the impact of each architectural component through ablation studies.

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