AdaPTS：將單變量基礎模型適應於概率性多變量時間序列預測

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting

February 14, 2025

作者: Abdelhakim Benechehab, Vasilii Feofanov, Giuseppe Paolo, Albert Thomas, Maurizio Filippone, Balázs Kégl

cs.AI

摘要

預訓練基礎模型（FMs）在單變量時間序列預測任務中展現了卓越的性能。然而，仍存在多項實際挑戰，包括處理特徵間複雜的依賴關係以及量化預測中的不確定性。本研究旨在通過引入適配器來解決這些關鍵限制；適配器作為特徵空間轉換，能夠有效利用預訓練的單變量時間序列FMs進行多變量任務。適配器的工作原理是將多變量輸入投影到合適的潛在空間，並獨立地對每個維度應用FM。受表示學習和部分隨機貝葉斯神經網路文獻的啟發，我們提出了一系列適配器及優化/推理策略。在合成和真實世界數據集上進行的實驗證實了適配器的有效性，相比基線方法，在預測精度和不確定性量化方面均顯示出顯著提升。我們的框架AdaPTS將適配器定位為一種模組化、可擴展且有效的解決方案，用於在多變量情境下利用時間序列FMs，從而促進其在現實世界應用中的更廣泛採用。我們在https://github.com/abenechehab/AdaPTS上公開了代碼。

English

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.