3 citations · 4 across the 4 of their papers we have counts for
4 papers · 1 filter
Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization
Luca Masserano, Abdul Fatir Ansari, Boran Han +8
How to best develop foundational models for time series forecasting remains an important open question. Tokenization is a crucial consideration in this effort: what is an effective…
Transferring Knowledge from Large Foundation Models to Small Downstream Models
Shikai Qiu, Boran Han, Danielle C. Maddix +3
How do we transfer the relevant knowledge from ever larger foundation models into small, task-specific downstream models that can run at much lower costs? Standard transfer learnin…
Cross-Frequency Time Series Meta-Forecasting
Mike Van Ness, Huibin Shen, Hao Wang +3
Meta-forecasting is a newly emerging field which combines meta-learning and time series forecasting. The goal of meta-forecasting is to train over a collection of source time serie…
GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics
Ke Alexander Wang, Danielle Maddix, Yuyang Wang
We consider the problem of probabilistic forecasting over categories with graph structure, where the dynamics at a vertex depends on its local connectivity structure. We present GO…