AdaPTS: Adapting Univariate Foundation Models to Probabilistic
  Multivariate Time Series Forecasting
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.
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.
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Abdelhakim Benechehab
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2025-02-17T10:50:21.800Z
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Abdelhakim Benechehab
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Vasilii Feofanov
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2025-02-18T09:32:27.648Z
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Vasilii Feofanov
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Giuseppe Paolo
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2025-02-21T14:43:09.340Z
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Giuseppe Paolo
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GPaolo
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Albert Thomas
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Maurizio Filippone
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Balázs Kégl
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2025-02-18T09:32:30.076Z
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Balazs Kegl
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balazskegl
AdaPTS: Adapting Univariate Foundation Models to Probabilistic
  Multivariate Time Series Forecasting
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.
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.
Adapters improve pre-trained univariate time series models for multivariate forecasting by transforming inputs and enhancing accuracy and uncertainty quantification.
pre-trained foundation models multivariate time series forecasting feature-space transformations latent space Bayesian neural networks adapters AdaPTS