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

arXiv:2609.26822 · cs.LG, q-fin.CP, q-fin.GN · Submitted 2026-09-20 · Read on arXiv

cs.LG, q-fin.CP, q-fin.GN

Submitted: 2026-09-20

Updated: 2026-09-20

Code: https://github.com/NabeelAhmad9/HARN

Terminology

• Ablation — 32× in this paper · explained in 1 episode
• Transformer — 14× in this paper · explained in 2 episodes
• Alignment — 12× in this paper · explained in 1 episode
• Aggregation — 8× in this paper · explained in 1 episode
• Associative Memory — 8× in this paper · explained in 2 episodes
• Consistency — 8× in this paper · explained in 3 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Provenance — 5× in this paper · explained in 3 episodes
• Time Series Forecasting — 3× in this paper · explained in 3 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Sparse Attention — 2× in this paper · explained in 5 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Cross-Attention Mechanism — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Heteroscedasticity — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Non-stationarity — 1× in this paper · explained in 2 episodes
• Probabilistic Forecasting — 1× in this paper · explained in 3 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• WaveNet: A Generative Model for Raw Audio
• Layer Normalization
• Gaussian Error Linear Units (GELUs)

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