title: AdaPTS: Adapting Univariate Foundation Models to Probabilistic
    Multivariate Time Series Forecasting
summary: 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.
paper_authors._id: 67b30f528904ba09ca9d9ab4
paper_authors.name: Abdelhakim Benechehab
paper_authors.status: extracted_confirmed
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paper_authors.fullname: Abdelhakim Benechehab
paper_authors.type: user
paper_authors.user: abenechehab
paper_authors._id: 67b30f528904ba09ca9d9ab5
paper_authors.name: Vasilii Feofanov
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paper_authors.fullname: Vasilii Feofanov
paper_authors.type: user
paper_authors.user: vasilii-feofanov
paper_authors._id: 67b30f528904ba09ca9d9ab6
paper_authors.name: Giuseppe Paolo
paper_authors.status: claimed_verified
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paper_authors.fullname: Giuseppe Paolo
paper_authors.type: user
paper_authors.user: GPaolo
paper_authors._id: 67b30f528904ba09ca9d9ab7
paper_authors.name: Albert Thomas
paper_authors._id: 67b30f528904ba09ca9d9ab8
paper_authors.name: Maurizio Filippone
paper_authors._id: 67b30f528904ba09ca9d9ab9
paper_authors.name: Balázs Kégl
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-18T09:32:30.076Z
paper_authors._id: 672cb69f250205b317235571
paper_authors.avatarUrl: /avatars/37afc1506b15a2f9c37e3e8769142580.svg
paper_authors.fullname: Balazs Kegl
paper_authors.type: user
paper_authors.user: balazskegl
paper_title: AdaPTS: Adapting Univariate Foundation Models to Probabilistic
    Multivariate Time Series Forecasting
paper_summary: 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.
paper_ai_keywords: feature-space transformations pre-trained foundation models (FMs) univariate time series forecasting tasks multivariate tasks learned space feature-space transformations time series FMs adapters optimization/inference strategies forecasting accuracy uncertainty quantification adaptive representation learning partially stochastic Bayesian neural networks multimodal representation AdaPTS