17 citations · 36 across the 16 of their papers we have counts for
18 papers
Morpheus-2.0: Multiscale Probabilistic Forecasting with Dual-Axis Attention
Aakriti, Manoj Cherukumalli, Prathamesh Patil +5
A universal forecaster is trained once and applied everywhere, so it must serve tasks whose schemas, forecast horizons, and temporal resolutions all differ. The number of targets a…
Dynamic Linear Coregionalization for Realistic Synthetic Multivariate Time Series
Annita Vapsi, Penghang Liu, Saheed Obitayo +8
Synthetic data is essential for training foundation models for time series (FMTS), but most generators assume static correlations, and are typically missing realistic inter-channel…
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…
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…
Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark
Elizabeth Fons, Rachneet Kaur, Soham Palande +4
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, clima…
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…
Multi-Modal Financial Time-Series Retrieval Through Latent Space Projections
Tom Bamford, Andrea Coletta, Elizabeth Fons +4
Financial firms commonly process and store billions of time-series data, generated continuously and at a high frequency. To support efficient data storage and retrieval, specialize…
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…
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…
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…
Augmenting transferred representations for stock classification
Elizabeth Fons, Paula Dawson, Xiao-jun Zeng +2
Stock classification is a challenging task due to high levels of noise and volatility of stocks returns. In this paper we show that using transfer learning can help with this task,…
Evaluating data augmentation for financial time series classification
Elizabeth Fons, Paula Dawson, Xiao-jun Zeng +2
Data augmentation methods in combination with deep neural networks have been used extensively in computer vision on classification tasks, achieving great success; however, their us…
A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing
Elizabeth Fons, Paula Dawson, Jeffrey Yau +2
The financial crisis of 2008 generated interest in more transparent, rules-based strategies for portfolio construction, with Smart beta strategies emerging as a trend among institu…