Go to ICLR 2025 Workshop SCOPE homepageAdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting
Track: Main paper track (up to 5 pages excluding references and appendix)
Keywords: Multivariate time series forecasting, Foundation models, Probabilistic inference
TL;DR: We introduce adapters to enhance pre-trained univariate time-series foundation models (FMs) for multivariate tasks, addressing feature dependencies and uncertainty quantification.
Abstract: Pre-trained foundation models (FMs) have shown exceptional performance in univariate time series forecasting tasks. However, several practical challenges persist, including managing intricate dependencies among features and quantifying uncertainty in predictions. This study aims to tackle these critical limitations by introducing **adapters**—feature-space transformations that facilitate the effective use of pre-trained univariate time series FMs for multivariate tasks. Adapters operate by projecting multivariate inputs into a suitable latent space and applying the FM independently to each dimension in a zero-shot manner. Inspired by the literature on representation learning and partially stochastic Bayesian neural networks, we present a range of adapters and optimization/inference strategies. Experiments conducted on both synthetic and real-world datasets confirm the efficacy of adapters, demonstrating substantial enhancements in forecasting accuracy and uncertainty quantification compared to baseline methods. Our framework, **AdaPTS**, positions adapters as a modular, scalable, and effective solution for leveraging time series FMs in multivariate contexts, thereby promoting their wider adoption in real-world applications. We release the code at https://github.com/abenechehab/AdaPTS.
Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.
Submission Number: 13
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