arXiv research

Study improves queue length estimation from connected vehicles by filtering parameters.

The paper develops algorithms to minimize queue length regret in a communication system.

In this paper, a novel joint transmit power and resource allocation approach for enabling ultra-reliable low-latency communication (URLLC) in vehicular networks is proposed. The objective is to minimize the network-wide power consumption of vehicular users (VUEs) while ensuring high reliability in terms of probabilisti…

Algorithm stabilizes queues in asymmetric systems with unknown service rates.

This paper attempts to find out numerically the distribution of the queue-length ratio in the context of a model of preferential attachment. Here we consider two restaurants only and a large number of customers (agents) who come to these restaurants. Each day the same number of agents sequentially arrives and decides w…

We introduce Delay Pruning, a simple yet powerful technique to regularize dynamic Boltzmann machines (DyBM). The recently introduced DyBM provides a particularly structured Boltzmann machine, as a generative model of a multi-dimensional time-series. This Boltzmann machine can have infinitely many layers of units but al…

New clustering method uses Wasserstein distance to analyze simulation outputs.

A learning-based algorithm optimizes admission control in a queuing system.

Designs for allocating resources to prioritize needy applicants while estimating treatment effects.

Paper improves volatility estimation using a Queue-Reactive model.

We consider the problem of approximating the stationary distribution of an ergodic Markov chain given a set of sampled transitions. Classical simulation-based approaches assume access to the underlying process so that trajectories of sufficient length can be gathered to approximate stationary sampling. Instead, we cons…

Study on queues with Hawkes arrivals, proving steady-state behavior and developing an efficient algorithm.

Unified model for market dynamics, linking price and order flow.

The paper analyzes transaction fees on blockchains using a priority queue model.

In this work we introduce two variants of multivariate Hawkes models with an explicit dependency on various queue sizes aimed at modeling the stochastic time evolution of a limit order book. The models we propose thus integrate the influence of both the current book state and the past order flow. The first variant cons…

Method uses deep learning to estimate traffic intensity.

Improved queue-reactive model considers order sizes for better market simulation.

MERLIN tackles multi-objective task scheduling with hierarchical DRL, outperforming existing methods.

Silent abandonment reduces contact center efficiency by 5%-15%.

Enhances queue-reactive model for realistic limit order book simulation.

Modern Internet services, such as those at Google, Yahoo!, and Amazon, handle billions of requests per day on clusters of thousands of computers. Because these services operate under strict performance requirements, a statistical understanding of their performance is of great practical interest. Such services are model…

The paper models blockchain queues and trading dynamics, finding conditions for transaction priority and price impact.

New estimator avoids overfitting in convex regression.

Order positions are key variables in algorithmic trading. This paper studies the limiting behavior of order positions and related queues in a limit order book. In addition to the fluid and diffusion limits for the processes, fluctuations of order positions and related queues around their fluid limits are analyzed. As a…

Finite-time queue peaks in stochastic networks have logarithmic scaling after geometric thresholds.

The paper tackles ride-hailing fleet repositioning with a calibrated demand approach.

Motivated by empirical data, we develop a statistical description of the queue dynamics for large tick assets based on a two-dimensional Fokker-Planck (diffusion) equation, that explicitly includes state dependence, i.e. the fact that the drift and diffusion depends on the volume present on both sides of the spread. "J…

Paper uses queue theory to model financial signals with relativistic delay.

The paper optimizes LLM inference systems through queueing theory.

In a financial market, for agents with long investment horizons or at times of severe market stress, it is often changes in the asset price that act as the trigger for transactions or shifts in investment position. This suggests the use of price thresholds to simulate agent behavior over much longer timescales than are…

This paper considers a cross-layer adaptive modulation system that is modeled as a Markov decision process (MDP). We study how to utilize the monotonicity of the optimal transmission policy to relieve the computational complexity of dynamic programming (DP). In this system, a scheduler controls the bit rate of the m-qu…

An online learning framework optimizes pricing and capacity in service systems.

Online class imbalance learning constitutes a new problem and an emerging research topic that focusses on the challenges of online learning under class imbalance and concept drift. Class imbalance deals with data streams that have very skewed distributions while concept drift deals with changes in the class imbalance s…

The paper improves regret bounds for admission control in queueing systems.

We investigate whether the bid/ask queue imbalance in a limit order book (LOB) provides significant predictive power for the direction of the next mid-price movement. We consider this question both in the context of a simple binary classifier, which seeks to predict the direction of the next mid-price movement, and a p…

Model for cross-border markets with limited transmission capacities.

Modeling high-frequency order book data with Hawkes-Markovian process.

Order submission and cancellation are two constituent actions of stock trading behaviors in order-driven markets. Order submission dynamics has been extensively studied for different markets, while order cancellation dynamics is less understood. There are two positions associated with a cancellation, that is, the price…

RL optimizes meta-order execution by adapting to market conditions.

We examine the dynamics of the bid and ask queues of a limit order book and their relationship with the intensity of trade arrivals. In particular, we study the probability of price movements and trade arrivals as a function of the quote imbalance at the top of the limit order book. We propose a stochastic model in an …

The study examines how backrun auctions can protect traders from price manipulation.

Gittins index optimizes decision-making under uncertainty, even in complex scenarios.

Study proves fluid limits of fragmented limit-order markets.

FourNet approximates financial transition densities using Fourier transforms.

We use the Minority Game as a testing frame for the problem of the emergence of diversity in socio-economic systems. For the MG with heterogeneous impacts, we show that the direct generalization of the usual agents' profit does not fit some real-world situations. As a typical example we use the traffic formulation of t…

Approximate Bayesian inference on the basis of summary statistics is well-suited to complex problems for which the likelihood is either mathematically or computationally intractable. However the methods that use rejection suffer from the curse of dimensionality when the number of summary statistics is increased. Here w…

New RL algorithm reduces regret in birth-death queueing problems.

Market makers face a trade-off between fill probability and post-fill returns, requiring contrarian strategies.