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

Yunzhong Qiu; Zhiyao Cen; Zhongyi Pei; Chen Wang; Jianmin Wang

cs.LGFeb 28, 2026

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

Organizations: School of Software, BNRist, Tsinghua University, China

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-art LTMs and widely used datasets demonstrate that TATO consistently and significantly improves domain-adaptive forecasting performance, achieving a maximum reduction in MSE of 65.4% and an average reduction of 13.6%. Moreover, TATO is highly efficient, typically completing optimization in under 2 minutes, making it practical for real-world deployment. The source code is available at https://github.com/thulab/TATO.

Figures & tables

Figure 1: Three examples illustrating how data transformations enhance LTM predictions. (a) Downsampling stabilizes noisy Moirai predictions on ETTm2. (b) Outlier detection and interpolation correct Timer’s misinterpretation of anomalies on Weather. (c) Differencing enables Chronos to capture trends on Exchange by inducing stationarity. They demonstrate the potential of transformation optimization for FrozenForecasting.

Figure 2: Overview of the TATO framework. The framework consists of three main stages: (1) Data preparation, where diverse augmentations are applied to input samples to improve robustness; (2) Optimization of time series transformations, where a black-box optimizer searches for effective transformation pipelines comprising various preprocessing operators (e.g., trimming, normalization, denoising); and (3) Two-stage pipeline selection, where candidate pipelines are first filtered via Pareto ranking on validation metrics, followed by weighted multi-indicator ranking to select the optimal transformation pipeline for frozen LTM forecasting.

Panel of Figure 3 — Figure 3: Distribution of MAE before and after applying TATO on three representative tasks. Across all datasets, TATO consistently shifts the error distribution toward lower values, indicating improved forecasting accuracy compared to the vanilla baseline.

Panel of Figure 4 — Figure 4: Scalability analysis of TATO. (a) MSE improvement with increasing transformation trials (fixed 100 samples). (b) MSE improvement with increasing sample size (fixed 100 trials). Performance consistently improves with more trials and data, ranging from 50 to 500 in both dimensions.

Panel of Figure 5 — Figure 5: Ablation study results. (a) Effect of removing key framework components on the reduction of MSE. (b) Effect of removing individual transformation operators on MSE reduction. Mean and median %Promotion are shown for each variant.

Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

Hyperparameter	Type	Value Range
trimmer_length	Discrete	[596, 696, …, 14*96]
sampler_factor	Continuous	[1, 2]
aligner	Categorical	{’none’, ’edge’, ’mean’}
scaler	Discrete	{’none’, ’standard’, ’robust’}
differencer_level	Discrete	[0, 1]
warper	Categorical	{’none’, ’log’}

Appendix

Table 4: Hyperparameters for Data transform

Time Series Forecasting

Time series forecasting predicts future values from ordered observations using statistical, neural, and probabilistic models to support decisions under uncertainty.

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Time series forecasting with pretrained foundation models has demonstrated strong zero-shot capabilities. However, achieving optimal performance on time series with short or negligible historical data in domain-specific applications typically requires adaptation via either fine-tuning or RAG. While fine-tuning is effective, it incurs substantial computational costs. This work explores RAG within univariate time series (Retrieval Augmented Generation) as a more efficient alternative, in particular RAF (Retrieval Augmented Forecasting), and introduces RAEF (Retrieval-Augmented Extended Forecasting), a model-agnostic method built upon RAF. RAEF incorporates key refinements to the retrieval and aggregation mechanisms: (1) direct retrieval in input-space rather than embedding-space, reducing inference overhead, and (2) concatenation-based aggregation that preserves temporal structure instead of averaging. Empirical evaluation across multiple benchmark datasets demonstrates that RAEF outperforms RAF in both accuracy and inference overhead. Furthermore, comprehensive comparisons with zero-shot and fine-tuned foundation models show that RAEF achieves competitive or superior performance to fine-tuning while avoiding its computational burden, establishing it as a practical and scalable approach for domain adaptation in time series forecasting.

Abstract

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