6 papers
MICA: Multivariate Infini Compressive Attention for Time Series Forecasting
Willa Potosnak, Nina Å»ukowska, MichaÅ WiliÅski +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…
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…
Exploring Representations and Interventions in Time Series Foundation Models
MichaÅ WiliÅski, 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…
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…
Implicit Reasoning in Deep Time Series Forecasting
Willa Potosnak, Cristian Challu, Mononito Goswami +3
Recently, time series foundation models have shown promising zero-shot forecasting performance on time series from a wide range of domains. However, it remains unclear whether thei…