7 papers · 1 filter
MICA: Multivariate Infini Compressive Attention for Time Series Forecasting
Willa Potosnak, Nina Żukowska, Michał Wiliński +4
Multivariate forecasting with Transformers faces a core scalability challenge: modeling cross-channel dependencies via attention compounds attention's quadratic sequence complexity…
STAMP: Spatial-Temporal Adapter with Multi-Head Pooling
Brad Shook, Abby Turner, Jieshi Chen +4
Time series foundation models (TSFMs) pretrained on data from multiple domains have shown strong performance on diverse modeling tasks. Various efforts have been made to develop fo…
TimeSeriesGym: A Scalable Benchmark for (Time Series) Machine Learning Engineering Agents
Yifu Cai, Xinyu Li, Mononito Goswami +3
We introduce TimeSeriesGym, a scalable benchmarking framework for evaluating Artificial Intelligence (AI) agents on time series machine learning engineering challenges. Existing be…
Investigating Compositional Reasoning in Time Series Foundation Models
Willa Potosnak, Cristian Challu, Mononito Goswami +4
Large pre-trained time series foundation models (TSFMs) have demonstrated promising zero-shot performance across a wide range of domains. However, a question remains: Do TSFMs succ…
Towards Long-Context Time Series Foundation Models
Nina Żukowska, Mononito Goswami, Michał Wiliński +2
Time series foundation models have shown impressive performance on a variety of tasks, across a wide range of domains, even in zero-shot settings. However, most of these models are…
Exploring Representations and Interventions in Time Series Foundation Models
Michał Wiliński, Mononito Goswami, Willa Potosnak +2
Time series foundation models (TSFMs) promise to be powerful tools for a wide range of applications. However, their internal representations and learned concepts are still not well…