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Deep learning framework NeuroMem-FHP enhances parameter estimation for fractional Hawkes process

By PulseAugur Editorial · [2 sources] ·

Researchers have developed NeuroMem-FHP, a deep learning framework designed to estimate parameters for the fractional Hawkes process (FHP). This framework utilizes Long Short-Term Memory (LSTM) and Transformer neural networks to directly infer model parameters from inter-arrival time sequences, bypassing computationally intensive likelihood optimization. Experiments show that the Transformer model achieves superior accuracy compared to LSTM and traditional Maximum Likelihood Estimation (MLE) on both synthetic and real-world datasets, including financial transaction data from Apple Inc. and emergency call records from Montgomery County. AI

IMPACT Offers a more accurate and efficient alternative to traditional methods for modeling event-driven systems with long-memory dynamics.

RANK_REASON The cluster contains an academic paper detailing a new deep learning framework for parameter estimation.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Deep learning framework NeuroMem-FHP enhances parameter estimation for fractional Hawkes process

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The cluster contains an academic paper detailing a new deep learning framework for parameter estimation.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Neha Gupta, Aditya Maheshwari ·

    NeuroMem-FHP: A Likelihood-Free Deep Learning Framework for Parameter Estimation of Fractional Hawkes Process

    arXiv:2607.11177v1 Announce Type: cross Abstract: In this paper, we propose deep learning based NeuroMem-FHP framework for estimating the parameters of the fractional Hawkes process (FHP), a self-exciting point process that captures long-range dependence through a fractional Mitt…

  2. arXiv stat.ML TIER_1 English(EN) · Aditya Maheshwari ·

    NeuroMem-FHP: A Likelihood-Free Deep Learning Framework for Parameter Estimation of Fractional Hawkes Process

    In this paper, we propose deep learning based NeuroMem-FHP framework for estimating the parameters of the fractional Hawkes process (FHP), a self-exciting point process that captures long-range dependence through a fractional Mittag-Leffler excitation kernel. Two neural architect…

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