# NeuroMemFPP: A recurrent neural approach for memory-aware parameter estimation in fractional Poisson process

arXiv: https://arxiv.org/abs/2512.05893 · HTML: https://mlsanity.com/paper/2512.05893/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 55 references that are in the map.
- First posted on arXiv 5 Dec 2025.

## Builds on (most cited first, 4 of 4)

- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [Physics-Informed Long Short-Term Memory for Forecasting and Reconstruction of Chaos](https://mlsanity.com/paper/2302.10779/) (2023)
- [Recurrent neural network based parameter estimation of Hawkes model on high-frequency financial data](https://mlsanity.com/paper/2304.11883/) (2023)
- [Parameter estimation for fractional Poisson processes](https://mlsanity.com/paper/1806.02774/) (2018)

## Built on by (most cited first, 1 of 1)

- [NeuroMem-FHP: A Likelihood-Free Deep Learning Framework for Parameter Estimation of Fractional Hawkes Process](https://mlsanity.com/paper/2607.11177/) (2026)

## Abstract (arXiv)

In this paper, we propose a recurrent neural network (RNN)-based framework for estimating the parameters of the fractional Poisson process (FPP), which models event arrivals with memory and long-range dependence. The Long Short-Term Memory (LSTM) network estimates the key parameters μ >0 and β ∈(0,1) from sequences of inter-arrival times, effectively modeling their temporal dependencies. Our experiments on synthetic data show that the proposed approach reduces the mean squared error (MSE) by about 55.3% compared to the traditional method of moments (MOM) and performs reliably across different training conditions. We tested the method on two real-world high-frequency datasets: emergency call records from Montgomery County, PA, and AAPL stock trading data. The results show that the LSTM can effectively track daily patterns and parameter changes, indicating its effectiveness on real-world data with complex time dependencies.