10 citations · 11 across the 4 of their papers we have counts for
13 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…