10 citations · 36 across the 18 of their papers we have counts for
19 papers
Privacy-Aware Time Series Synthesis via Public Knowledge Distillation
Penghang Liu, Haibei Zhu, Eleonora Kreacic +1
Sharing sensitive time series data in domains such as finance, healthcare, and energy consumption, such as patient records or investment accounts, is often restricted due to privac…
TS-Agent: Understanding and Reasoning Over Raw Time Series via Iterative Insight Gathering
Penghang Liu, Elizabeth Fons, Annita Vapsi +5
Large language models (LLMs) exhibit strong symbolic and compositional reasoning, yet they struggle with time series question answering as the data is typically transformed into an…
Towards Interpretable Time Series Foundation Models
Matthieu Boileau, Philippe Helluy, Jeremy Pawlus +1
In this paper, we investigate the distillation of time series reasoning capabilities into small, instruction-tuned language models as a step toward building interpretable time seri…
Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback
Yiyuan Yang, Zichuan Liu, Lei Song +6
Time series anomaly detection (TSAD) has traditionally focused on binary classification and often lacks the fine-grained categorization and explanatory reasoning required for trans…
AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation
Elizabeth Fons, Elena Kochkina, Rachneet Kaur +5
This paper explores the potential of large language models (LLMs) to generate financial reports from time series data. We propose a framework encompassing prompt engineering, model…
LSCD: Lomb-Scargle Conditioned Diffusion for Time series Imputation
Elizabeth Fons, Alejandro Sztrajman, Yousef El-Laham +3
Time series with missing or irregularly sampled data are a persistent challenge in machine learning. Many methods operate on the frequency-domain, relying on the Fast Fourier Trans…
Mixup Regularization: A Probabilistic Perspective
Yousef El-Laham, Niccolò Dalmasso, Svitlana Vyetrenko +2
In recent years, mixup regularization has gained popularity as an effective way to improve the generalization performance of deep learning models by training on convex combinations…
LOB-Bench: Benchmarking Generative AI for Finance -- an Application to Limit Order Book Data
Peer Nagy, Sascha Frey, Kang Li +5
While financial data presents one of the most challenging and interesting sequence modelling tasks due to high noise, heavy tails, and strategic interactions, progress in this area…
Variational Neural Stochastic Differential Equations with Change Points
Yousef El-Laham, Zhongchang Sun, Haibei Zhu +2
In this work, we explore modeling change points in time-series data using neural stochastic differential equations (neural SDEs). We propose a novel model formulation and training…
A Language Model-Guided Framework for Mining Time Series with Distributional Shifts
Haibei Zhu, Yousef El-Laham, Elizabeth Fons +1
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 shi…
Neural Stochastic Differential Equations with Change Points: A Generative Adversarial Approach
Zhongchang Sun, Yousef El-Laham, Svitlana Vyetrenko
Stochastic differential equations (SDEs) have been widely used to model real world random phenomena. Existing works mainly focus on the case where the time series is modeled by a s…
Augment on Manifold: Mixup Regularization with UMAP
Yousef El-Laham, Elizabeth Fons, Dillon Daudert +1
Data augmentation techniques play an important role in enhancing the performance of deep learning models. Despite their proven benefits in computer vision tasks, their application…
Synthetic Data Applications in Finance
Vamsi K. Potluru, Daniel Borrajo, Andrea Coletta +17
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality. We present a broad overview of prototypical applicatio…
MADS: Modulated Auto-Decoding SIREN for time series imputation
Tom Bamford, Elizabeth Fons, Yousef El-Laham +1
Time series imputation remains a significant challenge across many fields due to the potentially significant variability in the type of data being modelled. Whilst traditional impu…
Deep Gaussian Mixture Ensembles
Yousef El-Laham, Niccolò Dalmasso, Elizabeth Fons +1
This work introduces a novel probabilistic deep learning technique called deep Gaussian mixture ensembles (DGMEs), which enables accurate quantification of both epistemic and aleat…
DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift
Defu Cao, Yousef El-Laham, Loc Trinh +2
In electronic trading markets, limit order books (LOBs) provide information about pending buy/sell orders at various price levels for a given security. Recently, there has been a g…
StyleTime: Style Transfer for Synthetic Time Series Generation
Yousef El-Laham, Svitlana Vyetrenko
Neural style transfer is a powerful computer vision technique that can incorporate the artistic "style" of one image to the "content" of another. The underlying theory behind the a…
HyperTime: Implicit Neural Representation for Time Series
Elizabeth Fons, Alejandro Sztrajman, Yousef El-laham +2
Implicit neural representations (INRs) have recently emerged as a powerful tool that provides an accurate and resolution-independent encoding of data. Their robustness as general a…
Risk-Sensitive Compact Decision Trees for Autonomous Execution in Presence of Simulated Market Response
Svitlana Vyetrenko, Shaojie Xu
We demonstrate an application of risk-sensitive reinforcement learning to optimizing execution in limit order book markets. We represent taking order execution decisions based on l…