15 citations · 40 across the 11 of their papers we have counts for
12 papers · 1 filter
Scale-Invariant Training for Time Series Foundation Models
Ignacy Stepka, Willa Potosnak, Kin G. Olivares +1
Time series foundation models (TSFMs) are trained on large collections of time series datasets that span various morphologies and domains. This setting exposes models to series who…
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…
Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility
Willa Potosnak, Malcolm Wolff, Mengfei Cao +6
While accuracy is a critical requirement for time series forecasting, an equally important desideratum is reasonable forecast volatility across forecast creation dates (FCDs). Even…
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…
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…
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…
Global Deep Forecasting with Patient-Specific Pharmacokinetics
Willa Potosnak, Cristian Challu, Kin G. Olivares +2
Forecasting healthcare time series data is vital for early detection of adverse outcomes and patient monitoring. However, it can be challenging in practice due to variable medicati…
auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data
Chirag Nagpal, Willa Potosnak, Artur Dubrawski
Applications of machine learning in healthcare often require working with time-to-event prediction tasks including prognostication of an adverse event, re-hospitalization or death.…
The World as a Graph: Improving El Niño Forecasts with Graph Neural Networks
Salva Rühling Cachay, Emma Erickson, Arthur Fender C. Bucker +5
Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Niño-Southern Oscillation (ENSO). However, current dee…
Graph Neural Networks for Improved El Niño Forecasting
Salva Rühling Cachay, Emma Erickson, Arthur Fender C. Bucker +4
Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Niño-Southern Oscillation (ENSO). However, current dee…