1 citations · 1 across the 5 of their papers we have counts for
6 papers
Zero-shot Forecasting by Simulation Alone
Boris N. Oreshkin, Mayank Jauhari, Ravi Kiran Selvam +10
Zero-shot time-series forecasting holds great promise, but is still in its infancy, hindered by limited and biased data corpora, leakage-prone evaluation, and privacy and licensing…
A More Realistic Evaluation of Cross-Frequency Transfer Learning and Foundation Forecasting Models
Kin G. Olivares, Malcolm Wolff, Tatiana Konstantinova +8
Cross-frequency transfer learning (CFTL) has emerged as a popular framework for curating large-scale time series datasets to pre-train foundation forecasting models (FFMs). Althoug…
Modeling Human Spatial Mobility Patterns with the Lévy Flight Cluster Model
Malcolm Wolff, Adrian Dobra, Anton H. Westveld +1
Despite the extensive collection of individual mobility data over the past decade, fueled by the widespread use of GPS-enabled personal devices, the existing statistical literature…
SPADE-S: A Sparsity-Robust Foundational Forecaster
Malcolm Wolff, Matthew Li, Ravi Kiran Selvam +11
Despite significant advancements in time series forecasting, accurate modeling of time series with strong heterogeneity in magnitude and/or sparsity patterns remains challenging fo…
Using Pre-trained LLMs for Multivariate Time Series Forecasting
Malcolm L. Wolff, Shenghao Yang, Kari Torkkola +1
Pre-trained Large Language Models (LLMs) encapsulate large amounts of knowledge and take enormous amounts of compute to train. We make use of this resource, together with the obser…
LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data
Hanyu Zhang, Chuck Arvin, Dmitry Efimov +5
Modern time-series forecasting models often fail to make full use of rich unstructured information about the time series themselves. This lack of proper conditioning can lead to ob…