[2603.00629] Adapt Data to Model: Adaptive Transformation Optimization for Domain-shared Time Series Foundation Models

Computer Science > Machine Learning arXiv:2603.00629 (cs) [Submitted on 28 Feb 2026] Title:Adapt Data to Model: Adaptive Transformation Optimization for Domain-shared Time Series Foundation Models Authors:Yunzhong Qiu, Zhiyao Cen, Zhongyi Pei, Chen Wang, Jianmin Wang View a PDF of the paper titled Adapt Data to Model: Adaptive Transformation Optimization for Domain-shared Time Series Foundation Models, by Yunzhong Qiu and 4 other authors View PDF HTML (experimental) Abstract:Large time series models (LTMs) have emerged as powerful tools for universal forecasting, yet they often struggle with the inherent diversity and nonstationarity of real-world time series data, leading to an unsatisfactory trade-off between forecasting accuracy and generalization. Rather than continually finetuning new LTM instances for each domain, we propose a data-centric framework, time-series adaptive transformation optimization (TATO), that enables a single frozen pre-trained LTM to adapt to diverse downstream domains through an optimally configured transformation pipeline. Specifically, TATO constructs three representative types of transformations, including context slicing, scale normalization, and outlier correction, to help LTMs better align with target domain characteristics. To ensure robustness, we incorporate carefully selected time series augmentations and a two-stage ranking mechanism that filters out pipelines underperforming on specific metrics. Extensive experiments on state-of-the-a...