Search Results for author: Svitlana Vyetrenko

Found 26 papers, 2 papers with code

Variational Neural Stochastic Differential Equations with Change Points

no code implementations1 Nov 2024 Yousef El-Laham, Zhongchang Sun, Haibei Zhu, Tucker Balch, Svitlana Vyetrenko

We develop two methodologies for modeling and estimating change points in time-series data with distribution shifts.

Change Point Detection Time Series

A Language Model-Guided Framework for Mining Time Series with Distributional Shifts

no code implementations7 Jun 2024 Haibei Zhu, Yousef El-Laham, Elizabeth Fons, Svitlana Vyetrenko

Effective utilization of time series data is often constrained by the scarcity of data quantity that reflects complex dynamics, especially under the condition of distributional shifts.

Language Modelling Time Series +1

Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark

no code implementations25 Apr 2024 Elizabeth Fons, Rachneet Kaur, Soham Palande, Zhen Zeng, Tucker Balch, Manuela Veloso, Svitlana Vyetrenko

Large Language Models (LLMs) offer the potential for automatic time series analysis and reporting, which is a critical task across many domains, spanning healthcare, finance, climate, energy, and many more.

Time Series Time Series Analysis

LLM-driven Imitation of Subrational Behavior : Illusion or Reality?

no code implementations13 Feb 2024 Andrea Coletta, Kshama Dwarakanath, Penghang Liu, Svitlana Vyetrenko, Tucker Balch

We make an assumption that LLMs can be used as implicit computational models of humans, and propose a framework to use synthetic demonstrations derived from LLMs to model subrational behaviors that are characteristic of humans (e. g., myopic behavior or preference for risk aversion).

Imitation Learning

Neural Stochastic Differential Equations with Change Points: A Generative Adversarial Approach

no code implementations20 Dec 2023 Zhongchang Sun, Yousef El-Laham, Svitlana Vyetrenko

Given a time series dataset, the proposed method jointly learns the unknown change points and the parameters of distinct neural SDE models corresponding to each change point.

Change Point Detection Time Series

Augment on Manifold: Mixup Regularization with UMAP

no code implementations20 Dec 2023 Yousef El-Laham, Elizabeth Fons, Dillon Daudert, Svitlana Vyetrenko

Evaluations across diverse regression tasks show that UMAP Mixup is competitive with or outperforms other Mixup variants, show promise for its potential as an effective tool for enhancing the generalization performance of deep learning models.

Data Augmentation Deep Learning +1

INTAGS: Interactive Agent-Guided Simulation

no code implementations4 Sep 2023 Song Wei, Andrea Coletta, Svitlana Vyetrenko, Tucker Balch

To adapt to any environment with interactive sequential decision making agents, INTAGS formulates the simulator as a stochastic policy in reinforcement learning.

Algorithmic Trading Causal Inference +4

MADS: Modulated Auto-Decoding SIREN for time series imputation

no code implementations3 Jul 2023 Tom Bamford, Elizabeth Fons, Yousef El-Laham, Svitlana Vyetrenko

Time series imputation remains a significant challenge across many fields due to the potentially significant variability in the type of data being modelled.

Imputation Time Series

Deep Gaussian Mixture Ensembles

no code implementations12 Jun 2023 Yousef El-Laham, Niccolò Dalmasso, Elizabeth Fons, Svitlana Vyetrenko

This work introduces a novel probabilistic deep learning technique called deep Gaussian mixture ensembles (DGMEs), which enables accurate quantification of both epistemic and aleatoric uncertainty.

Deep Learning Probabilistic Deep Learning

K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs

no code implementations23 Feb 2023 Andrea Coletta, Svitlana Vyetrenko, Tucker Balch

Finally, by clustering the explanations we show that we are able to identify different agent policies and group observations accordingly.

Clustering Imitation Learning

DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift

no code implementations17 Nov 2022 Defu Cao, Yousef El-Laham, Loc Trinh, Svitlana Vyetrenko, Yan Liu

Using the proposed synthetic dataset, we provide a holistic analysis on the forecasting performance of three different state-of-the-art forecasting methods.

Benchmarking Time Series +1

StyleTime: Style Transfer for Synthetic Time Series Generation

no code implementations22 Sep 2022 Yousef El-Laham, Svitlana Vyetrenko

In this work, a novel formulation of time series style transfer is proposed for the purpose of synthetic data generation and enhancement.

Data Augmentation Style Transfer +4

HyperTime: Implicit Neural Representation for Time Series

no code implementations11 Aug 2022 Elizabeth Fons, Alejandro Sztrajman, Yousef El-Laham, Alexandros Iosifidis, Svitlana Vyetrenko

We show how these networks can be leveraged for the imputation of time series, with applications on both univariate and multivariate data.

Data Augmentation Imputation +2

Efficient Calibration of Multi-Agent Simulation Models from Output Series with Bayesian Optimization

no code implementations3 Dec 2021 Yuanlu Bai, Henry Lam, Svitlana Vyetrenko, Tucker Balch

Multi-agent simulation is commonly used across multiple disciplines, specifically in artificial intelligence in recent years, which creates an environment for downstream machine learning or reinforcement learning tasks.

Bayesian Optimization Time Series Analysis

Learning who is in the market from time series: market participant discovery through adversarial calibration of multi-agent simulators

no code implementations2 Aug 2021 Victor Storchan, Svitlana Vyetrenko, Tucker Balch

In electronic trading markets often only the price or volume time series, that result from interaction of multiple market participants, are directly observable.

Time Series Time Series Analysis

Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set

no code implementations27 May 2021 Yuanlu Bai, Tucker Balch, Haoxian Chen, Danial Dervovic, Henry Lam, Svitlana Vyetrenko

Stochastic simulation aims to compute output performance for complex models that lack analytical tractability.

SIM-GAN: Adversarial Calibration of Multi-Agent Market Simulators.

no code implementations1 Jan 2021 Victor Storchan, Svitlana Vyetrenko, Tucker Balch

In this paper, we present SIM-GAN -- a multi-agent simulator calibration method that allows to tune simulator parameters and to support more accurate evaluations of candidate trading algorithm.

Get Real: Realism Metrics for Robust Limit Order Book Market Simulations

no code implementations10 Dec 2019 Svitlana Vyetrenko, David Byrd, Nick Petosa, Mahmoud Mahfouz, Danial Dervovic, Manuela Veloso, Tucker Hybinette Balch

Machine learning (especially reinforcement learning) methods for trading are increasingly reliant on simulation for agent training and testing.

Risk-Sensitive Compact Decision Trees for Autonomous Execution in Presence of Simulated Market Response

no code implementations5 Jun 2019 Svitlana Vyetrenko, Shaojie Xu

We demonstrate an application of risk-sensitive reinforcement learning to optimizing execution in limit order book markets.

Q-Learning reinforcement-learning +2

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