5 papers · 1 filter
Twice Upon a Time: Complementary Representations for Time-Series Reasoning
Hedi Zisling, Liran Nochumsohn, Ilan Naiman +2
Time Series Language Models (TSLMs) enable language-based understanding and reasoning over temporal data, yet a fundamental question remains: what information should their time-ser…
DiffSDA: Unsupervised Diffusion Sequential Disentanglement Across Modalities
Hedi Zisling, Ilan Naiman, Nimrod Berman +2
Unsupervised representation learning, particularly sequential disentanglement, aims to separate static and dynamic factors of variation in data without relying on labels. This rema…
Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting
Liran Nochumsohn, Raz Marshanski, Hedi Zisling +1
Time series forecasting (TSF) is critical in domains like energy, finance, healthcare, and logistics, requiring models that generalize across diverse datasets. Large pre-trained mo…
One-Step Offline Distillation of Diffusion-based Models via Koopman Modeling
Nimrod Berman, Ilan Naiman, Moshe Eliasof +2
Diffusion-based generative models have demonstrated exceptional performance, yet their iterative sampling procedures remain computationally expensive. A prominent strategy to mitig…
A Multi-Task Learning Approach to Linear Multivariate Forecasting
Liran Nochumsohn, Hedi Zisling, Omri Azencot
Accurate forecasting of multivariate time series data is important in many engineering and scientific applications. Recent state-of-the-art works ignore the inter-relations between…