17 citations · 19 across the 6 of their papers we have counts for
9 papers
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…
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…
TADACap: Time-series Adaptive Domain-Aware Captioning
Elizabeth Fons, Rachneet Kaur, Zhen Zeng +4
While image captioning has gained significant attention, the potential of captioning time-series images, prevalent in areas like finance and healthcare, remains largely untapped. E…
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…
Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation
Elizabeth Fons, Paula Dawson, Xiao-jun Zeng +2
Data augmentation methods have been shown to be a fundamental technique to improve generalization in tasks such as image, text and audio classification. Recently, automated augment…