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
Venue: ICLR 2026 Poster (2026)
Keywords: time series foundation models
transformation optimization
time series forecasting
Primary area: other topics in machine learning (i.e., none of the above)

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.

Published as a conference paper at ICLR 2026
ADAPT DATA TO MODEL: ADAPTIVE TRANSFORMA- TION OPTIMIZATION FOR COMAIN-SHARES TIME SE- RIES FOUNDATION MODELS
Yunzhong Qiu', Zhiyao Cen", Zhongyi Pei', Chen Wang, Jianmin Wang School of Software, BNRist, Tsinghua University, China {qiuyz24, cenzy23 @maalss........ a singhua. edu. en (peizhyi, wang chen, jinnnjimmmne jimwang) e ts wawggnuinaa....m. www.ghaa.du.cc.
ABSTRACT
Large time series models (LTMs) have emerged as powerful tools for univer- sal forecasting, yet they often struggle with the inherent diversity and nonsta- tionarity of real-world time series data, leading to an unsatisfactory trade-off between forecasting accuracy and generalization. Rather than continually fine- tuning new LTM instances for each domain, we propose a data-centric frame- work, 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 nor- malization, and outlier correction, to help LTMs better align with target domain characteristics. To ensure robustness, we incorporate carefully selected time se- ries 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 signif- icantly improves domain- adaative forecasting performance, achieving a maxi- mum reduction in MSE of 65.4% and an average reduction of 13.6%. Moreover, TATO is highly efficient, typically completing optimization under 2 minutes, making it practical for world deployment. The source code is available at https://githuu: com www.//////whaaaaa.....ooo
INTRODUCTION
With the rapid advancement of large language models (LLMs), there has been growing interest in extending the capabilities large models to time series forecasting, i.e. large time series mod- els (LTMs) (Liu et al., 2024b). LTMs are designed to handle multiple downstream tasks using single foundational model. Two main technical directions have been widely adopted. The first involves building time series native models pre-trained on large-scale time series data (Liu al.) 2024b) Fatir Ansari et al.] 2024: Woo et al.) 2024 Garza & Mergenthaler-Canseee
(023). The sec- ond adapts large language models (LLMs) pre-trained on diverse text data time series analysis tasks (Gruver et al. 2024a Zhou et al. 2023 Liu et al. 2024a). These large models demonstrate significantly better generalization than traditional models, effectively supporting few-shot learning, in-context learning, and even zero-shot inference.
This capability, known zero-shot forecasting (Gruver al. 2024b), cutting-edge paradigm that enables accurate predictions on unseen data without additional training, much like using Chat- GPT. However, LTMs face significantly greater challenges than L.LMs in achieving effective zero- shot forecasting for time series. The fundamental issue lies in the inherent diversity of time series cafa--iifferen domains exhibit distinct temporal characteristics, making it extremely difficult for single LTM to generalize across all domains. A straightforward approach to improving domain-
'qqual contribution Corresponning author

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specific performance is to finetune pretrained LTMs, but this compromises the model's generaliz- ability and incurs prohibitive computational costs as the number of target domains increases.
To address this challenge, propose novel paradigm for applying LTMs that adapts data to shared foundation model for different domains. This approach strikes a balance between achieving strong performance across diverse domains and minimizing training costs through efficient data preprocessing, enabling a single LTM to serve all domains effectively. We term this paradigm Frozen Foundation Model-bass Nomain-Shared Forecasting (FrozenForeccasting). in which the foundation model remains frozen while concurrent lightweight data adaptations are allowed to handle diverse downstream domains.
The focus on model-centric development has long overlooked the potential of adapting the data it- self-a perspective that our work seeks revitalize. For decades, deep learning has been dominated by an end-to-end paradigm, where researchers have primarily focused on pretraining or finetuning models to improve performance. This trend has continued in recent work on LTMs, leaving the value of data transformation underexplored. To illustrate the impact of time series transformations on LTMs, we present three motivating examples that compare original LTM predictions with those with optimized transformations in Figure] T
Each example, on a distinet LTM and typical dataset, illustrates represen- tative case in which the transformation has a significant impact. In Example 1, Moirai (Woo et al] (224)) produces noisy predictions due to limited input context. By extending the input and ap- plying downsampling to emphasize pe- riodicity, the predictions become sig- nificantly more stable. In Example 2, Timer (Liu et al. (224b)) initially mis- interprets outliers as periodic patterns. After applying k-sigma outlier detection and linear interpolation, the predictions align closely with the ground truth trend. In Example 3, Chronos (Fatir Ansari al., 2024) generates conservative pre- dictions that regress toward the input mean. Applying differencing induces stationarity, enabling the model to better capture overall trends. These examples demonstrate that targeted data transfor- mations can substantially enhance LTM performance, motivating the need for automated transformation optimization in FrozenForeeasting.
Vanilla Predictions
Transformations Optmized
Example 1: Moiral large model on ETTm2 dataset
samping
Example 2: Timer-UTSD model on Weather dataset
denoising
Example 3: Chronos-tiny model Exchange dataset
differencing
Ground Truth
Ground Truth Our Predictions
Vanilla Predictions
Three examples illustrating how data transfor- mations enhance LTT predictions. (a) Downsampling stabilizes noisy Moirai predictions on ETTm2. (b) Out- lier detection and interpolation correct Timer's misinter- pretation of anomalies on Weather. (c) Differencing en- ables Chronos capture trends on Exchange by inducing stationarity. They demonstrate the potential of transfor- mation optimization for FrozenForecasting.
Figure 1:
To enable effective FrozenForecasting, we introduce an automated framework that optimizes time series transformations for LTMs. The proposed approach formulates transformation discovery hyperparameter optimization problem to search for the most effective preprocessing pipeline. To ensure robustness, we incorporate data augmentation during large-scale search. Each optimization trial selects and applies a combination of representative transformation operators- -uchh as context slicing, scale normalization, and outlier correction-to the input time series. A two-stage evaluation mechanism is then employed to identify the optimal transformation pipeline, tailored specifically for FrozenForecasting with LTMs. In summary, this paper makes the following major contributions:
New Paradigm for LTMs. We introduce FrozenForecasting, a novel paradigm that ad- dresses the practical requirement of deploying large time series models (LTMs): a single frozen model that generalizes across all domains. To enable this, we propose TATO (Time- series Adaptive Transformation Optimization), a preprocessing-basee based framework that pro- vides a universally beneficial, aggnssic solution for enhancing frozen LTM perfor- mance across diverse domains.
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Specialized Transformation Search Space. We carefully curate comprehensive set of time series transformations, forming a compact yet expressive search space for TATO to discover optimal preprocessing pipelines. This space encompasses three categories of performance-critica operators: context slicing, scale normalization, and outlier correction, each designed to mitigate domain-specific data characteristics.
Remarkable Empirical Results. Through extensive experiments on eight popular time se- ries datasets, we demonstrate that TATO consistently and significantly improves the perfor- mance of several state-of-the-art LTMs. Our approach achieves a maximum mean squared error (MSE) reduction of 65.4% and average reduction of 13.6%. while maintaining exceptional efficiency, with optimization typically completed in under 2 minutes.
2 RELATED WORK
2.1 LARGE TIME SERIES MODELS
Unlike traditional models such as Autoformer (Wu al. 2021) and PatchTST (Nic et al. 2022, which are typically trained and applied to specific datasets, LTMs are designed as foundational mod- els capable of zero-shot forecasting. Some LTMs trained from scratch large-scale time series datasets. For instance, TimeGPT (Garza & Mergenthaler-Came
2023) claims to be the first time series foundation model capable of handling multiple tasks datasets with single model. Timer (Liu et al. 2024b) trained a GPT-style architecture on the constructed Unified Time Series Dataset (UTSD) and demonstrated notable feasibility and generalizability across various tasks. Moirai (Woo) iet al. 2024), trained on the Large-scale Open Time Series Archive (LOTSA), excels in zero-shot forecasting and handles multivariate time series. TimesFM (Das al.
2023) uses decoder-style attention mechanism with input patching for forecasting tasks. Other LTMs typically leverage ex- isting LLMs and adapt them time series tasks. For example, Chronos (Fatir Ansari et al.] 2024), finetuned from LLMs, tokenizes time series data using a fixed vocabulary. AutoTTmmes Times (Liu et al.) 2024a) leverages LLMs to align time series with natural language. However, given the extremely high diversity and non-stationarity of time series downstream domains, the generalizability of LTMs is severely limited, as shown in Figure []] Finetuning is a practical approach to applying LTMs to downstream domains. It creates new, targeted LTMs based on target data, thereby increasing the number of LTM instances. Instead, TATO modifies how LTMs process data, transforming it to improve FrozenForecasting performance via automated transformation pipelines. Compared to finetuning, TATO offers a novel paradigm for balancing specificity and generalization in LTMs, enhancing specificity without compromising generalizability.
2.2 TIME SERIES TRANSFORMATION
Time series transformations Tawakuli et al. 2024) are crucial for enhancing data quality and im- proving model performance, particularly training data mining applications. Normalization techniques, such as the Z-score (Alcxandropoulos et al. 2019) and P-norm (Zheng & Casari 2018)), widely used in machine learning to rescale numeric features, producing values with similar ranges. Data cleaning addresses data quality issues by identifying and managing anomalies and out- liers, which can negatively impact model performance. The interquartile range (IQR) (Wang al.) 2018) and the local outlier factor (LOF) (Breunig et al.) 2000) are widely used methods for identify- ing and handling outliers. Sensor fusion techniques like the Kalman Filter (KF) (Julier & Uhlmann 1997]. discretization methods such as K-means clustering (Gupta et al. 2010). and feature extraction methods like principal component analysis (PCA) (Abdi Williams 2010) all serve to integrate, summarize, and reduce the dimensionality of time series data. The above methods provide ways to handle the variety of time series, which can be used as operators within the TATO framework.
To enhance sample diversity during transformation optimization, TATO leverages range of data augmentations commonly used in time series analysis (Iglesias et al. 2023 Jaaeri et al., 202 These include flipping augmentations (magnitude flip and time flip) that reverse sequences mag- nitudes to create new variations (Um et al. 2017 Fawaz et al.. 2018), warping augmentations (mag- nitude warp and time warp) that distort data simulate different conditions (Um al. 2017] Park al.
et al. 202T). noise augmentations (EWMA smoothing and jitter) that improve data resilience (Flores et al.
2021 Rashid & Louis 2019, and translation that shifts data horizontally
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vertically simulate distribution shifts (Huang et al. 2017). Importantly, these augmentations are applied exclusively during the optimization phase to enrich the candidate pool: the final selected transformation pipeline for inference does not include any augmentation.
2.3 TEST-TIME ADAPTATION
Test-time adaptation (TTA) techniques address performance degradation caused by distribution shifts between training and test data by adapting models during inference (Liang et al. 20224). Tra- ditional TTA approaches typically involve self-supervised training of neural networks test data (Zhang et al. 2022 Schneider et al., 2020, [wasawa & Matsuo 2021 [Boudia et al. 2022).
unlabeled
More recently, cansformation-basee methods have emerged for time series models. 'TokenMerg- ing (Götz et al. 2024) applies preprocessing to reduce token count in transformer-base
time series models, improving inference throughput. NewNorm (Kim al. 2024) addresses distribution shifts through trend estimation and self-supervised learning of new normalities. While these methods tar- get specific challenges, TATO takes broader approach by systematically optimizing series of transformations to enhance frozen LTM performance across diverse domains core requirement of the Frozen Forecstinggg paradigm.
METHOD
3.1 THE PARADIGM OF ADAPTING DATA TO MODEL
To address the practical need to deploy LTMs across diverse domains, introduce FrozenFore- casting, a paradigm that performs domain-shared forecasting using a single frozen LTM. Within this paradigm, propose TATO, an automated framework that adapts data rather than models, optimizing time series transformations to enhance frozen LTM performance while avoiding costly, sometimes risky finetuning.
definition 1 (Frozen Foundation Model-based Domain-shared Forecasting). A forecasting paradigm in which a single pre-trained foundation model remains frozen during inference, while lightweight, domain- specifcc adaptations are applied to imput data enable accurate foreeasting across diverse downstream domains.
To enable FrozenForecasting with existing LTMs, we propose TATO (Time-series Adaptive Trans- formation Optimization), framework that systematically discovers and applies optimal transfor- mation pipelines to improve forecasting performance across diverse domains while the model itself remains frozen. Let D be domain of time series, from which two subsets Distory and D Future
are sampled. H denotes configuration space for time series transformation pipelines and h denotes one instance of the space. Given large time series model M, time series adaptive transformation optimization for FrozenForecasting (TATO) can be formulated as follows:
definition 2 (Time-series Adaptive Transformation Optimization for FrozenForccasting).
min L(M, Dhistory: h)) hEH
where L(M, D history: h) denotes the loss that the model M achieves when the sampled data D history are transformed by h. It should be noted that the optimal trunsformation pipeline h is searched on Dhistory. while Dfuture is used for final testing.
While TATO leverages historical data, it differs fundamentally from finetuning several key as- pects. First, the LTM remains completely frozen- moozen-oly the data transformation pipelines are opti- mized, leaving model parameters untouched. This enables a single model to adapt to diverse domain distributions without the one-to-one mapping required by finetuning. Second, TATO is substantially more efficient, requiring far less historical data. our experiments, just 500 samples are sufficient for TATO to achieve considerable improvements across various scenarios. To safeguard against data shift during optimization, we carefully selected diverse time series augmentations that maintain suf- ficient variety while preserving each sample's intrinsic characteristics. As result, transformation pipeline optimization carries a significantly lower risk of overfitting than finetuning, underpinning TATO's universal effectiveness.
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3.2 THE TATO FRAMEWORK
The TATO framework operates through three core stages: data preparation, transformation pipeline optimization, and optimal pipeline selection, as shown in Figure 2
Preparation
Optimization of Time Transformations
Lookbock
box Optimization
Optimal Pipelins
Second- stage
Smeeth
Negetade
Diverse Augmentations
Window
Trme Kerp
Transformation Pipeline
Trimming
Exponh
Transformation Operators
Trimmer
Contextual Transformaaionns
recnde-ante
Sampler
Aligner
Mann USSE Eech The
wy 718811558) nead0arke
Parsto besedd
mak/M/M5) neb
First stage Rank
Outlier Transformations
Denolter
Impatator
Tranyar
recederable
Denoising
none
Normalizing Transformations
MBC
Scaler
Offferncer
*4 eaggge campler?
Performance Evaluation
Nome
Frozen ProzeenForecatting
MiX
Scaling
stbaddrdd
Timer
Motral
Chrones
Augrennted Dota
mi mox
Series Nodels
GroundTruth
Sample
pretrainesion lage
Tredetimss
highl generaliabll
processing
Lookback
Grouns Truth
sensitivee transfomaaions
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 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 validation metrics, followed by weighted multi-indicator ranking to select the optimal transformation pipeline for frozen LTM forecasting.
3.2.1 DATA PREPARATION
As defined in Definition 2 the optimal transformation pipeline ht* is identified from historical sam- ples but tested on unseen data. To address potential discrepancies between Distory and future we aim to maximize Diistory" 's diversity by incorporating range of augmentation methods. The augmentation methods used in TATO are carefully selected from the literature (Iglesias et af.) 2023 Javeri et al. 2021 Zhang et al., 2022), including both commonly used methods and those designed to generate samples that are vastly different (c.g., spikes, slopes) and may occur in real-world scenar- ios. Flipping augmentations, including magnitude and time flip, reverse the sequence or magnitude of data points to create new variations (Um et al. [017]] Fawaz et al.) 2018). Warping augmentations, such as magnitude warp and time warp, distort data to simulate different conditions (Um al., 2017] Park et al. 22099 Jeong et al. 2021). Noise injections include exponentially weighted moving aver-
age (FWMA) for smoothing and jitter for adding random noise (Flores al. 2022 Rashid & Louis) 2019). Translation shifts data horizontally or vertically to simulate distribution shifts, addressing
mean offsets in test data (Huang et al., 2017). Adding slopes (Wen & Keyes 2019) can simulate datasets with underlying trends.
3.2.2 TRANSFORMATION PIPELINE OPTIMIZATION
The transformation pipelines consist of nine operators categorized into three types: (1) contextual transformations that slice the inference context, (2) normalization transformations that adjust the value range of imputs and outputs, and (3) outlier transformations that mitigate the risk of perfor- mance degradation due outliers.
All data transformations consist of a pair of pre-process and post-process operators applied before the input to LTMs and after the output from LTMs. Such operators are predefined by set of hyper- parameters to control the implemented data transformation. They are optimized by hyperparameter optimization techniques, such the widely used Bayesian optimization algorithm Tree-structured
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During the specific optimization process, the data transformation order is also considered using rule-based heuristics and search-tree treee branch-cutting control overall computational complexity. After fixed number of trials, the opti- mal data transformation pipelines are identified using Pareto-based ranking across multiple assess- ment metrics ensure robust selection. The technical details of data transformations presented in Appendix |A.3
Parzen Estimator (TPE) (Bergstra et al., 2011 Watanabe 2023
To prevent inappropriate or repeated trials caused by meaningless reordering, we predefine three orders as candidates rather than searching all possible orders. The predefined transformations are ordered according several heuristic rules. First, regarding efficiency, the operator Trimmer is placed at the head because it may reduce the sample length for subsequent operators. Second, outlier transformations should generally precede normalizing transformations, as outlier values can significantly affect normalization. Third, some operators, such as Aligner, prefer to be placed in the tail to avoid introducing interference to others.
3.2.3 TWO-STAGE PARETO-BASED RANKING OF PERFORMANCE
To quantitatively evaluate forecasting performance, we compute multiple error metrics on the trans- formed samples, including mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean squared percentage er- ror (MSPE). For enhanced robustness, we also consider their summary madistics-median standard deviation (STD), and interquartile range (IQR). A key challenge is that no single trial consistently outperforms the others across all metrics, making averaging across metrics a risk of elevating poorly performing trials. To address this, we adopt two-stage Pareto-based ranking approach (Palakonda) & Mallipeddi] 2017) for selecting the final transformation pipeline.
In the first stage, we filter out risky trials that underperform any metric subset across all aug- mented samples. Specifically, we construct Pareto set in which each retained trial must not rank poorly any given metric combination across the augmented data, thereby eliminating trials that excel only on specific metrics. For efficiency, we set the filtering threshold to retain 16 candidates and exclude the remaining trials for each metric subset. In the second stage, we rank the candidate transformation pipelines using only the original samples, not the augmented ones. The ranking is computed weighted sum of all metric ranks, with higher weights assigned to MSE and MSPE. The top-ranked transformation pipeline is then applied to test data for Forzeanoree..iing. Further details are provided in Appendix A.4
4 EXPERIMENTS
4.1 DATASETS AND BASELINES
Five major datasets are included in our experimental analysis: ETT (4 subsets), Electricity, Ex- change, Traffic, and Weather. Specifically, we follow the same data preparation and train- train-validation- test set split protocol used in TimesNet (Wu et al. 2022). Only 2% or less of the total training samples used in TATO, and the test set is consistently held out during optimization of the trans- formation pipeline. Several advanced transformer based LTMs are introduced in our assessment. Timer(Liu et al. 2024b) employs GPT-style architecture and benefits from extensive pre-training. enabling it to autoregressively predict the next time series token in a unified generative manner. Moirai Woo al. 2024). masked encoder-based universal time series forecasting transformer trained on LOTSA, demonstrates superior performance as zero-shot forecaster compared to full- shot models. Chronos(Fatir Ansari et al. (024) tokenizes time series values using scaling and quan- tization into fixed vocabulary and trains existing transformer-base language model architectures on these tokenized time series using cross-entropy loss.
4.2 EVALUATION METHOD
To comprehensively evaluate the effectiveness of TATO, we employ a set of error metrics and their statistical measures assess the relative performance improvements achieved through data transformations. Mean Squared Error (MSE) and Mean Absolute Error (MAE) are used to quantify the performance of the time series forecasting. The distributional properties of the metrics, such as

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the mean and median, are computed to provide a robust measure of central tendency and to reduce the influence of outliers. We also used the standard deviation (STD) to measure the spread around the mean and the interquartile range (IQR) to represent the middle half of the error values, providing additional perspectives for evaluating robustness.
To assess the effectiveness of optimized data transformations, compare the error metries with and without applying TATO. Let Cv be the error metric of the vanilla baselines, following the transfor- mations from the LTMs and lookback length of 7 96 from Timer[Liu et al. 2024b). Let eT be the error metric using optimized transformations. We define a metric, Relative Percentage Promotion in Error, as follows:
definition 3 (Relative Percentage Promotion in Error). Relative Percentage Promotion in Er- ror( Promotion) from baseline vanilla to our method TATO is calculated as "/rromotion ey- A positive Prromotion value indicates performance enhancement.
4.3 EFFECTIVENESS
We evaluate TATO's 's effectiveness by reporting the relative percentage improvement in error (%Pro- motion) achieved over vanilla frozen LTMs across various models, datasets, and prediction horizons in Table I The experiments cover 192 distinct scenarios (8 datasets x 4 prediction lengths x 8 LTMs), demonstrating the universality of our approach. For robustness, each experiment repeated three times. TATO uniformly samples 500 training instances per dataset to optimize transformation pipelines, with a budget of up to 500 trials per scenario.
Table 1: MSE and MAE reduction achieved by TATO across different models and datasets. Results are averaged over prediction horizons 24, 48, 96, 192. Positive %Promotion, in bold, indicates performance improvement (error reduction) achieved by TATO compared to the baseline frozen LTM. Best results in each row are highlighted in red, worst in blue.
Models
Timer-UTTD
Timer-LOTSA
Moiral-small
Moirai-hase
Mairai-targe
Chronos-ting Blay
Average
Error Metrie
MSE
MAF
MSE
MAR
MSE
MAE
MSK
MAF
MAF
MSE
MAR
MSE
MAR
Vanilla
0-4096
04472
0.5010
0.4628
0.5163
0.4001
0.5349
0.4381
0,4000
0.4371
0.5055
0.4458
0,5052
ETTbl
with TATO
0,3901
0.4762
0.4399
0.5010
0.4850
0.5064
0.4503
0.5081
0:4141
0.8823
0.4850
0,5036
0.4307
0,1950
&Promotion
4.8%
1.7%
40.2%
0.00
3.6%
2.3%
8.1%
3.2%
5.5%
3.3%
-2.0%
0.4%
3.4%
1.8%
Vanilla
0.3258
0.4413
0.3646
0.4777
0.5349
0.5660
0.4657
0.5287
0.4444
0.5119
0.4181
0.4892
0.4247
0.5025
BTTH2
with TATO
0.3200
0.475
0.3431
0.4535
0.1691
0.5344
0.4538
0.5211
0.3658
0,6692
0.4297
0.500)
0.3975
0.4860
% Promotion
1.7%
0.9%
5.9%
5.1%
10.6%
5.6%
2.6%
1.4%
17.9%
8,3%
-285%
-2.2%
6.4%
3,3%
Vanilla
0,296
0.3979
0.3687
0,4550
0.2554
0.3682
012648
0.3701
0.2500
0.3549
0.2815
0.3833
0.2841
0.3882
67
with TATO
0,2800
0.3910
0,2180
0. 3583
0.3350
0.3467
0.3256
0.3478
0:2253
0.3445
0.2457
0.356
0.2380
0,3500
&Promotion
2.5%
1.8%
40.6%
25.7%
11.6%
5.8%
13.7% %
6.0%
5.3%
2.9%
12.7%
7.0%
15.9%
888%
Vanilla
0.0644
0.5224
0.5146
0.4166
0.2969
0.3996
0.3747
0.391
0.3288
0.3964
0.4006
0.4307
0.3633
0.4345
ETTm2
with
0.4368
0.2506
0.3520
0.3485
0.3477
0,2772
&Promotion
5.9%
20.3%
15.5%
20.1%
33,0%
18.3%
35.3%
20.0%
23.7%
Vanilla
0.1753
0:1714
0.5761
0.5749
0,6352
0.3632
0.4216
Elee.
with TATO
0,1729
0.2859
0.1710
0.2885
0.5539
0.5566
0.4434
0,4949
0.5100
0,5350
0.2716
0,3629
033538
0.4266
Promution
1.4%
1.6%
0.3%
0.3%
15.4%
11.7%
23.0%
13.0%
24.8%
15.8%
0.6%
0.1%
16.1%
9.3%
Vanilla
0,5190
0.5004
0.8317
0,7013
0.2897
0.3855
0.5524
0.3615
0,2450
0.3570
0.4013
0.4604
0.4232
0.4577
Exch.
with TATU
0,3077
0.3905
0.2881
0,3727
0.2997
0.3801
0.2570
0.3586
0.2635
0,3073
0.2529
0.3543
0,2748
0.3796
&Promotion
40.8%
22.0%
65.4%
46.9%
3.5%
1.4%
-1 15
0.5%
-7.5%
-2.0%
37.0%
19.6%
35.1%
19.0%
Vanilla
0.0605
0.1380
0.0616
0. 0.1404
0.1646
0.4982
0.3364
0.4600
0.3049
0,4233
0.0763
0.1548
0,2007
0 3840
Traffsc
with TATO
0.0589
0.1253
0,0602
0.1384
0.3326
0.4663
0.2743
0.4112
0.2519
0,3766
0.0769
0.1554
0.1758
0.2805
% Promotion
2,5%
2.8%
2,3%
1.5%
8.8%
6.4%
18.5%
12.3%
17.4%
11.0%
-0.9%
-0.4%
12.4%
7.7%
Vanilla
0.4937
0.4967
0.5858
0.5533
0.4984
0.4785
0.4920
0.4606
B.5433
0,4058
0,7282
0.5692
0.5573
0,5090
Weather
with TATO
0,6048
0.5290
0,5898
0.534
0.1964
0.1814
0.1961
0.4687
0,5107
0
0,5980
0.5245
0,5690
0,5033
&Prmmotinn
22.55
-6.5%
40.2%
0,8%
-1,8%
2,8%
17.9%
7.9%
1.5%
Vanilla
0,5431
0.4050
0.5923
0,4419
0.4185
0.4804
0.4066
0.4661
0,4031
0,4594
0.3770
0.4170
0,9901
0.4456
Aserage
with TATO
0.3225
0.3920
0,2050
0.3723
0.9799
0.4513
0.5568
0.433
0.3465
0.4228
0.3225
0. 0.9877
0.9372
0.4097
% Promotion
6.0%
4.1%
24.8%
15.7%
9.2%
6.0%
12.2%
7.3%
14.0%
8.0%
14.5%
7.0%
13.6%
8.1%
Table demoostraass that TATO commonly matches or outperforms vanilla baselines in 84.3% of the 96 evaluated cases. The MSE improvements range from 6.0% to 24.8% across different LTMs, with an average reduction of 13.6%. Across all LTMS in each dataset, TATO consistently outperforms the vanilla baselines, with the lowest improvement of 1.5% on Weather and the greatest of 35.1 % on Exchange. Notably, TATO delivers substantial gains precisely in cases where vanilla baselines perform poorly (highlighted in blue), validating our motivation that ineffective predictions can be effectively mitigated through adaptive transformation optimization. In a few cases, such as Timer- UTSD Weather, Moirai-laree gains, suggesting that even with adaptive transformation optimization, distribution shifts can still pose challenges. We leave further investigation of these edge cases future work.
Exchange, and Chronos- tiny ETTh2, TATO exhibits limited
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Besides, we present the distribution shifts of MAE in three representative tasks caused by TATO in Figure B The MAE distribution of vanilla, shown in the yellow bars, shifts to TATO's 's blue bars over smaller, narrower range, indicating significant performance improvement.
Timell/150 model on Exchange datiased
Mairsi-lange mudel in Electricky detased
Chrens-lity model ETli22 detesel
of Proilctnn
Veniis
Vanilla
10una0 of Pro0icc
Vandia
Our
Nean Abvolute Ento (MAE]
Mwan Abuohite troo IMAE] (b) Moirai-large Error.
Mwan Absolate Erer (MAC)
(a) Timer-UTSSE Error.
(c) Chronos-ting Error.
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.
4.4 EFFICIENCY
To evaluate efficiency, we calculate the overhead introduced by TATO during the optimization pro- cess. Table presents the overhead data for three representative models: Timer-UTSD, Moirai-base, and Chronos-ting with fixed prediction lengths of 96. The experiments were conducted under nearly identical settings, except for the number of trials and samples. The number of trials controls the op- timization budget, and the number of samples may vary according to the data conditions in practice.
Table 2:
Overhead in TATO optimization phase across different models and configurations. Even when configured with 500 trials or 500 samples, the time overhead remains reasonably acceptable.
Trials
Samples
Model
25
100
500
25
100
500
Timer-UTSD
2.97
11.21
76.56
6.80 s
11.21
37.44 s
Moirai-hase
5.61 s
18.05
106.40
11.39 s
18.05
65.90 s
Chronos-tiny
29.05 s
1 s
S73.30
87.71 s
1
398.03
Three different numbers of trials are used in the first part of Table t to illustrate the changes in overhead over trials, with the sample size fixed 100. Similarly, three different sample sizes are used in the second part of Table illustrate the changes in overhead across samples, with the trials fixed at 100. During the optimization phase, the primary source of overhead is the additional model inference required by our method to evaluate the effectiveness of data processing. which is heavily influenced by the model's inference time. Most experiments can be completed within 120 seconds, which is highly efficient compared to finetuning ditional training. Chronos takes longer because it relies on LLMs. In general, the optimization overhead scales linearly with the number of optimization trials and samples.
test-time adaptation methods that require ad-
In the inference phase, we assess the overhead of transformations for different batch sizes. During the inference phase, extra overhead comes from the data transformations selected by TATO. When the batch size is 1, the additional overhead is under 3 milliseconds, indicating that the tailored transformation operators are highly efficient relative to model inference. The extra overhead varies slightly across models because different transformations are selected in different scenarios.
4.5 ENHANNCNNG UNIVERSALLY FINETUNED LTMS
we compare TATO with finetuning in terms of the trade-off between specificity and generalization. One way to preserve a single LTM's generalizability of finetuning is to train jointly across all downstream domains. In this experiment, finetuned models to further demonstrate its effectiveness. We finetune Timer-UTSD and Timer- LOTSA separately all eight datasets, using 500 samples per dataset, obtain a single LTM
In Section 2.1
apply TATO to such universally
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that generalizes well across domains. We then TATO optimize transformation pipelines for each finetumed model. As shown in Table 3 TATO further improves forecasting performance, on average, beyond the finetuned baselines. For Timer-UTSD finetuned on all datasets, TATO achieves a 4.5% reduction in MSE and a 4. % reduction in MAE. For Timer-L.OTS A finetuned on all datasets, TATO achieves 9.9% reduction in MSE and 6.5% reduction in MAE. On average, TATO delivers a 7.3% MSE improvement and a 5.3% MAE improvement. These results demonstrate that TATO and finetuning address complementary aspects of domain adaptation: while finetuning adjusts model parameters to capture cross- cross-domain patterns. TATO further enhances specificity by optimizing data transformations for each domain, together enabling more effective FrozenForecasting.
Table 3: Results of prediction using TATO upon finetuning. The average results under prediction lengths of (24,48,96,19))) are reported. Positive % Promotion indicates performance enhancement.
ETTM
KTT12
BTTml
KTTm2
Flec.
Exch.
Traffic
Weather
Avernge
MSE
MIS
MSE
MAE
0.3621
0,4692
0.5015
0.4311
0.3458
0.3592
0,2245
04063
6.2576
0 0.5934
0.4382
0.437
0J0222
11:955
[U3852
0.5:0e
0:3119
0.401S
Times LOTSA
0.3567
0,4005
0.2449
0.4235
0.2540
0,5730
0.2731
0.3964
6.2677
0.3672
0,4090
0.8566
0.1001
0.2133
0.888
0,4577
0.3156
0.4M5
with TATO
0.3957
0,4751
0 0.3000
0.4211
0.3317
0.300]
112309
0.3421
0.5618
0 8.767
0.2928
0.375
0.0928
0.3019
0.4757
0.4807
0.2849
0.5780
Premetion
+188
-1.0%
+1.7%
-1.1%
8,8
6.1%
14.1%
0.00%
37.6%
13.7%
7.3%
5.4%
2.5%
2.6%
0.9%
6.5%
0.1644
0,4899
2981
0/4271
5.3677
0.521
112714
0.4013
0.857
0.5536
9-4852
0.0962
013913
0.0967
0.9002
0.3142
0.4073
with TATO
0:3851
0.0988
1 29996
0.4277
0.3182
0.5989
013367
0.198
0.3519
01 1571
0.3075
0. 3.388
048915
1963
115180
0.4997
0.2015
0.9816
5 SPretoxtion
0.25
0.01%
00.4%
08.19
4.2%
3,3%
9.5%
10.7%
4.1%
1.8%
33.1%
20.7%
5.0%
3,9%
-3.5%
0.55
7.3%
5.3%
4.6 ANALYSIS EXPERIMENTS
The analysis experiments were conducted on three representative LTMs, as shown in Table 2 with fixed prediction length of 96. We first examine TATO's scalability by varying the number of transformation trials and the number of data samples. The impact of specific transformations or pipeline steps is also analyzed.
4.6.1 SCALABILITY
We conducted scalability experiments determine the minimum required sample and trial sizes to achieve significant performance gains, and to explore the upper bounds on improvement achievable through data transformations. Figure Aalure/aashow shows the effect of increasing the number transformation trials on MSE reduction (% (%Promoion), with fixed sample size of 100. Results show a clear positive trend, with MSE improvement increasing from 7.2% with 50 trials to 9.4% with 500 trials, indicating that
extensive search consistently yields better transformations. Figure 4b examines the impact of sample size on optimization effectiveness, with a fixed budget of 100 traals. As more samples become available, both the mean improvement and its stability increase, with the MSE reduction increasing from 4.6% with 50 samples to 8.8% with 500 samples, while the variance notably decreases. These findings confirm that TATO benefits from both more trials and more data, with practical resource requirements well within affordable ranges.
Scalability
Scalability
12.0%
12.0%
Promottion
%roomttion
10.0%
8.0%
8.0%
6.0%
6.0%
4.0%
MSE Mean NPromotion
MSE Mean NPromotion
2.0%
MSE Median S:Promation
2.0%
MSE Median Nihromttoon
50
100
175
250
375
500
50
100
175
250
375
500
Transform Trials
Data Samples
(a) Transformation trials
(b) Data Samples
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). Perfor- mance consistently improves with more trials and data, ranging from 50 to 500 in both dimensions.

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4.6.2 ABLATIONS
We conducted ablation studies to identify the effects of transformations and steps in TATO's frame- work. Figure pressens ablation studies evaluating the contribution of each transformation operator and framework component to TATO's overall performance. The full configuration, incorporating all operators (Section A.3) and steps (Section 3.2). achieves the best mean and median MSE reduction, confirming that each element contributes positively to forecasting accuracy and stability. Notably, removing the Trimmer or Scaler operators results in a substantial performance drop, underscoring their critical role in preparing data for frozen LTMss. Interestingly, omitting the Denoiser operator or the Stagtage step yields greater mean improvement but the cost of increased variance and lower median performance. This suggests that while some components may be particularly benefi- cial for specific tasks, they also help ensure robustness across diverse scenarios. Overall, the results validate the necessity of TATO's complete design for achieving consistent and reliable gains.
Ablation Effects of Pipeline Steps
Ablation Effects of Transform Operators
MSE Mean NPrommtion
MSE Mean NPPromttion
SsPromoEoon
10%
Median Prommmtion
%6romation
10%
MSE Median SPrometion
899
8%
6%%
4%%
2%,
0%
0%
wub MultiMttric wi Murtis atistics
All
w/o Aurmment antmentaaioon
TeinStageRank
w/s Racrder
w/c TPE
w/o Trimmer
w/o Sampler
weo Aligner
we Normalzee mazzer
wi Warper
wio Difference enferencee
we Imputates
Danaiser
wio Cipper
(a) Steps
(b) Transformations
Figure 5: Ablation study results. (a) Effect of removing key framework components the reduction of MSE. (b) Effect of removing individual transformation operators on MSE reduction. Mean and median &Promotion are shown for each variant.
The ablation results reveal an interesting trade-off off between mean performance and robustness. While removing Denoiser reduces the average MSE, the increased variance and lower median suggest that it plays crucial role in stabilizing predictions across diverse samples. Similarly, the TwoStageRank mechanism, despite slightly reducing mean gains, proves essential for consistent performance by eliminating trials that excel only specific metrics. This validates our design philosophy: robust- ness across domains is as important as peak performance in FrozenFoorecasting.
CONCLUSION
In this paper, introduced TATO, automated framework that optimizes transformation pipelines to enable effective FrozenForecasting with pretrained LTMs. Through nine tunable operators ad- dressing context, scale, and outliers, combined with two-stage Pareto-based ranking mechanism, TATO consistently improves forecasting performance across diverse domains. Extensive experi- ments demonstrate tion typically under two minutes. These results validate TATO as practical, lightweight solution for unlocking the full potential of frozen LTMs without costly finetuning. Future work includes extend- ing TATO to multivariate settings, expanding the set of operators for specialized domains, exploring adaptive operator selection, and integrating test-time adaptation to enhance robustness to distribu- tion shifts. We believe this data-centric perspective opens promising avenues for more sustainable and generalizable deployment of large models across real-world applications.
average MSE reduction of 13.6% and maximum of 65.4%, with optimiza-
ACKNOWLEDGMENTS
This work is supported by the National Key Research and Development Program of China (2033YB33080003), the BNRist Project, and the National Engineering Research Center for Big Data Software. We also gratefully acknowledge Mengren Zheng's 's contributions to the additional experiments during rebuttal.
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REFERENCES
Hervé Abdi and Lynne Williams. Principal component analysis. Wiley interdisciplinary reviews: computational statistics, 2(4):433-459, 2010.
comaatio-AAgeloo N Alexandropoulos, Sotiris B Kotsiantis, and Michael N Vrahatis. Data prepro- cessing in predictive data mining. The Knowledge Engineering Review, 34:e1, 2019.
James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. Algorithms for hyper- parameer optimization. Advances in neural information processing systems, 24, 2011.
Samit Bhanja and Abhishek Das. Impact of data normalization deep neural network for time series forecasting. arXiv preprint 0rXiv:1812.05511 2018.
Ane Blázquez-Gareía. Angel Conde, Usue Mori, and Jose A Lozano. A review on outlier/anomaly detection time series data. ACM computing surveys (CSUR), 54(3):1-33 2021.
Malik Boudiaf, Romain Mueller, Ismail Ben Ayed, and Luca Bertinetto. Parameter-fiee free online test- time adaptation. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 8344-8353, 2022.
George EP Box and David R Cox. An analysis of transformations. Journal of the Royal Statistical Society Series B: Statistical Methodology, 26(2):211-24,) 1964.
Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng. and Jörg Sander. Lof: identifying density- based local outliers. In Proceedings of the 2000 ACM SIGMOD international conference Management of data, pp. 93-104, 2000.
Robert Goodell Brown. Statistical forecasting for inventory control. (No Tite),, 1959.
Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou. A decoder-only foundation model for time-series forecasting. arXiv preprint arXiv:2310. 10688, 2023.
Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, al. Chronos: Learning the language of time series. arXiv e-prints, pp. arXiv-2403, 2024..
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, and Pierre-Alain Muller. Data augmentation using synthetic data for time series classification with deep residual networks. arXiv preprini arXiv:1808.00 000..0800.5.... 2018.
Anibal Flores, Hugo Tito-Chura, and Honorio Apaza-Alanoca. Data augmentation for short-term time series prediction with deep learning. In Intelligent Computing: Proceedings of the 2021 Computing Conference, Volume pp. 492-506. Springer, 2021.
Azul Garza and Max Mergenthaler-Cansseo.. Timegpt-1, arXiv preprint arXiv:2310.0358) 2023.
Leon Götz, Marcel Kollovich, Stephan Günnemann, and Leo Schwinn. Efficient time series processing for transformers and state-space models through token merging. arXiv preprint arXiv:2405.17951, 2024.
Nate Gruver, Mare Finzi, Shikai Qiu, and Andrew G Wilson. Large language models are zero-shot time series forecasters. Advances in Neural Information Processing Systems, 36, 2024a.
Nate Gruver, Mare Finzi, Shikai Qiu, and Andrew Gordon Wilson. Large Language Models Are Zero-Shot Time Series Forecasters, June 2024b. URL 07820 arXiv:2310.07820 [cs].
Ankit Gupta, Kishan Mehrotra. and Chilukuri Mohan. A clustering- discretization for su- pervised learning. Statistics & probability letters, (009-10):816-111 2010.
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700-4708, 2017.
11

---

Published as a conference paper at ICLR 2026
Guillermo Iglesias, Edgar Talavera, Ángel González-Prieto, Alberto Mozo, and Sandra Gómez- Canaval. Data augmentation techniques in time series domain: a survey and taxonomy. Neural Computing and Applications, (5(14):10123-10111 2023.
Yusuke Iwasawa and Yutaka Matsuo. Test-time classifier adjustment module for model-agnostic do- main generalization. Advances in Neural Information Processing Systems, 34:2427-2440, 2021.
Indrajeet Y Javeri, Mohammadhossein Toutiaee, Ismailcem B Arpinar, John A Miller, and Tom W Miller. Improving neural networks for time-series forecasting using data augmentation and au- toml. In 2021 IEEE Seventh International Conference on Big Data Computing Service and Ap- plications (Biaeett))))iie), pp. 1-8. IEEE, 2021.
Chi Yoon Jeong. Hyung Cheol Shin, and Mooseop Kim. Sensor-data augmentation for human activity recognition with time-warping and data masking. Multimedia Tools and Applications, 80 (14):29911-21099, 2021.
Simon Julier and Jeffrey K Uhlmann. New extension of the kalman filter to nonlinear systems. In Signal processing, sensor fusion, and target recognition VI, volume 3068, pp. 182-193. Spic, 1997.
Dongmin Kim, Sunghyun Park, and Jaegul Choo. When model meets new normals: Test-time adap- tation for unsupervised time-series anomaly detection. In Proceedings of the AAAI Conference Artificial Intelligence, volume 38, pp. 13113-13121, 2024.
Denis Kwiatkowski, Peter CB Phillips, Peter Schmidt, and Yongcheol Shin. Testing the null hy- pothesis of stationarity against the alternative of a unit root: How sure we that economic time series have a unit root? Journal of econometrics, 54(1-3):159-178, 1992.
Ming-Chang Lee, Jia-Chun Lin, and Ernst Gunnar Gran. How far should we look back to achieve effective real-time time-series anomaly detection? In International Conference on Advanced Information Networking and Applications, pp. 148. Springer, 2021.
Jian Liang, Ran He, and Tieniu Tan. A comprehensive survey test-time adaptation under distri- bution shifts. International Journal of Computer Vision, pp. 1-34, 2024.
Minhao Liu, Ailing Zeng, Muxi Chen, Zhijian Xu, Qiuxia Lai, Lingna Ma, and Qiang Xu. Scinet: Time series modeling and forecasting with sample convolution and interaction. Advances in Neural Information Processing Systems, 35:5816-5822 2022.
Yong Liu, Guo Qin, Xiangdong Huang, Jianmin Wang, and Mingsheng Long. Autotimes: Au- toregressive time series forecasters via large language models. arXiv preprint arXiv:2402.02370, 2024a.
Yong Liu, Haoran Zhang, Chenyu Li, Xiangdong Huang, Jianmin Wang, and Mingsheng Long- Timer: Transformers for time series analysis at scale. arXiv preprint arXiv:2402.085 02368, 2024b.
Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. time series is worth 64
words: Long-term forecasting with transformers. arXiv preprint arXiv:2211. 14730, 2022.
Vikas Palakonda and Rammohan Mallipeddi. Pareto dominance-based algorithms with ranking methods for many-objective optimization. IEEE Access, 5:11043-1105,, 2017.
Daniel S Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D Cubuk, and Quoe V Le. Specaugment: A simple data augmentation method for automatic speech recognition. arXiv preprint /904x:/904.087 08779, 2019.
Khandakar M Rashid and Joseph Louis. Window-warping: a time series data augmentation of imu data for construction equipment activity identification. In ISARC. Proceedings of the interna- tional symposium on automation and robotics in construction, volume 36, pp. 651-657. IAARC Publications. 2019.
MR Saradjian and M Akhoondzadeh. Thermal anomalies detection before strong earthquakes (mg 6.0) using interquartile, wavelet and kalman filter methods. Natural Hazards and Earth System Sciences, (1(4): 1099-1009911088 2011.
12

---

Published as a conference paper at ICLR 2026
Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, and Matthias Bethge. Improving robustness against common corruptions by covariate shift adaptation. Ad- vances in neural information processing systems,
2020.
Amal Tawakuli, Bastian Havers, Vincenzo Gulisano, Daniel Kaiser, and Thomas Engel. Survey: time-series data preprocessing: a survey and an empirical analysis. Journal of Engineering Re- search, 2024.
Terry T Um, Franz MJ Pfister, Daniel Pichler, Satoshi Endo, Muriel Lang, Sandra Hirche, Urban Fietzek, and Dana Kulić. Data augmentation of wearable sensor data for parkinson's 's disease monitoring using convolutional neural networks. In Proceedings of the 19th ACM international conference on multimodal interaction, pp. 216-220, 2017.
Wang, J Caja, and E Gómez. Comparison of methods for outlier identification surface charac- terization. Measurement, 117:312-325, 2018.
Shuhei Watanabe. Tree-structured parzen estimator: Understanding its algorithm components and their roles for better empirical performance. arXiv preprint arXiv:230..11127, 2023.
Tailai Wen and Roy Keyes. Time series anomaly detection using convolutional neural networks and transfer learning, arXiv preprint wrxiv:1955.13628 2019.
Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, and Doyen Sa- hoo, Unified training of universal time series forecasting transformers. arXiv preprint arXiv:2402.02592, 2024.
Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. Autoformer: Decomposition trans- formers with auto-correlation for long-term series forecasting. Advances neural information processing systems, 34:22419-22453 2021.
Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long. Timesnet: Tem- poral 2d-variation modeling for general time series analysis. arXiv preprint arXiv:2210002186. 2022.
Marvin Zhang, Sergey Levine, and Chelsea Finn. Memo: Test time robustness via adaptation and augmentation. Advances neural information processing systems, 35:38629-38643 2022.
Alice Zheng and Amanda Casari. Feature engineering for machine learning: principles and tech- niques for data scientists. O'Reilly Media, Inc.", 2018.
Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. Informer: Beyond efficient transformer for long sequence time-series forecasting, In Proceedings of the AAAI conference on artificial intelligence, volume 35, pp. 11106-1115,, 2021.
Tian Zhou, Peisong Niu, Liang Sun, Rong Jin, et al. One fits all: Power general time series analysis by pretrained Im. Advances in neural information processing systems, 36:43322 43355, 2023.
A APPENDIX
A.I THE DETAILS OF USAGE OF LLMS
We use the Pro version of Grammarly to check spelling and polish complex sentences. LLMs are probably used inside Grammarly. We did not use LLMs in any other way this paper.
A.2 IMPLEMENTATION DETAILS
All experiments were conducted on a server equipped with Intel(R) Xeon 14-core processors, 384GB of RAM, and 8 NVIDIA TITAN X (Pascal) GPUs, running Ubuntu 18.04.
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A.2.1 DATASET DETAILS
We evaluate the models on diverse set of time series datasets from real scenarios, including (1) ETT et al. (02T) (Electricity Transformer Temperature) comprising hourly data from ETThl and ETTh2 and 15-minute data from ETTml and ETTm2, collected between July 2016 and July 2018, including load and oil temperature. (2) Electricity containing hourly electricity consumption data of 321 customers from 2012 to 2014. (3) Exchange 3 recording daily exchange rates of eight countries from 1990 to 2016. (4) Traffic a collection of hourly road occupancy rates measured by sensors on San Francisco Bay area freeways. (5) Weathe containing meteorological data recorded
every 10 minutes throughout 2020, including 21 indicators like air temperature and humidity.
When possible, we follow the same data preparation and vrain-validation-tes set split protocol used in TimesNet(Wu et al. 2022). TATO optimizes the transformation pipelines on frozen LTMs using only a limited subset (e.g.. 100 or 500 samples) of the training set. The data splitting is consistent with TimesNet to ensure fair comparison. For instance, ETTml and ETTm2 have datasets split (34465, 11521, 1 11521) for training, validation, and testing. Only less than 2% of the total training samples used in TATO, and the test set is consistently kept unseen during the optimization of the transformation pipeline.
In time series forecasting, short-term forecasting tasks usually use 12.24444488 as prediction lengths, while long-term forecasting tasks usually use {96, 192,336,700; We wanted to cover both scenarios in a reasonable experiment workload, so 94,4,,,,6119) is picked. For the forecasting settings, we fix the prediction length to vary in {24,48, 192}. Our univariate prediction exper- iments focus exclusively on each dataset's OT column, representing the most meaningful data for real-world production scenarios.
A.2.2 LARGE TIME SERIES MODELS
Several advanced transformer-bat based LTMs designed for univariate time series forecasting have been proposed recently. Timer(Liu et al. 2024b) employs GPT--tyyle architecture and benefits from extensive pre-training, enabling it to autoregressively predict the next time series token in a uni- fied generative manner. Timer-UTSD and Timer-LOTSA maintain the same training configuration but are trained on different datasets: the Unified Time Series Dataset (UTSD) and the Large-scale Open Time Series Archive (LOTSA), respectively. Moirai(Woo et al. 2024). masked encoder- based universal time series forecasting transformer trained on LOTSA. demonstrates superior per- formance as zero-shot forecaster compared to full-shot models. Chronos(Patr Ansari et al. 2024) tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer- based language model architectures on these tokenized time series using cross-entropy loss. The inference parameters of the LTMs were fixed. Timer followed the settings in the original paper, with patch_sit size of 96. Moirai used a patch. of 128 and num num_ samples of 20. Chronos used petch_len of 512 and num_samples of 3.
A.2.3 HYPERPARAMETER SPACE
The hyperparameters in the table are designed to optimize various data transformation operations for time series forecasting models. For example. the trimmer length discrete hyperparameter that controls how much past data is retained, with values ranging from 5 to 14 times 96 and an original setting of times 96. The sampler_facto is a continuous hyperparameter that adjusts the downsampling upsampling rate, with range of I to 2, starting with a value of 1, meaning sampling initially. The aligner is a categorical hyperparameter that determines the padding method, offering options like 'none", 'edge', and scaler is used to select the normalization technique, with is a
choices like "none", 'standard', and 'robust', also defaulting to 'none'. The differencer discrete hyperparameter that specifies the order of differencing, either 0 1, with O indicating no differencing. The warper governs the application of warping techniques, such as the 'log' transfor- mation, with 'none' as the default setting. The mullierdetect determines how outliers are identified, offering options like 'none', 33sigmm", and "1.5.igr', with 'none' as the starting point. The denoiser
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specifies the denoising technique, such as 'none' or 'ewma', with the default being 'none'. Finally, the srrpper-factor sets the range for clipping extreme values, with options like 'none', '0.5", and '1', starting with 'none' unless clipping needed. These hyperparameters tailored to enhance model performance by refining the input data through various preprocessing techniques.
Table 4: Hyperparameters for Data transform
Hyperparameter
Type
Value Range
trimmer length
Discrete
[5*96, 6*96, ... 14*96]
sampler.factor factor
Continuous
[1
aligner
Categorical
"none", 'dgee, 'mean") "none", 'standard', 'robust')
scaler
Discrete
differencer level
Discrete
[0. II
warper
Categorical
"none', 'log')
outlier_dee _detect
Categorical
'none", "3 3_sigma', '1.5 (..'iqr)} 'none', 'ewma')
denoiser
Categorical
clipper_facte
Categorical
nonee. '0.5",
A.3 TIME SERIES TRANSFORMATIONS
The recent LTMs generally adopt several fixed data transformations. They generally embed time series points using patching techniques. Some LTMs segment data into multiple scales to extract more features and employ stochastic sampling to generate robust forecasting distributions. The original transformations of the LTMs are typically carefully designed in accordance with the pre- training datasets. However, the pretraining datasets may differ from the test data, making LTMs sensitive to the transformations. The proposed framework, TATO, enhances the reliability of LTMs by employing time-series transformation optimizations rather than a fixed sequence of transforma- tions. TATO's candidate transformations involve nine operators of three types. TATO can be easily extended to other transformations. However, a vast search space poses a significant challenge. We carefully selected the nine transformations cover the three most representative dimensions of transformation, and they exhibit a strong capability of enhancing LTMs in experiments.
A.3.1 CONTEXTUAL TRANSFORMATTONNS
The first is the use of contextual transformations to slice inference inputs, such as adjusting the lookback length, downsampling, and patch completion. The operator Trimmer adjusts the look- back length to approach the fit range for the forecasting task (Lee et al. (02T). A longer lookback means more information from recent history. Generally, more information better. However, too much information could interfere with LTMs, resulting in poor performance. We denote Trimmer as follows:
Trimmer (x, Pi) = x[len(x) P1 len(x)]
(1)
where is a lookback sample whose length denoted by lxn(x), and P1 represents the hyper- paramter of lookback length.
The operator Sampler modifies the frequency of data points to find an optimal balance between de- tails and overall trend (Liu et al. (022). A choice of Sampler, Downsampling, can reduce noise and highlight trends and cycles, whereas another choice Upsampling intends to provide more detailed data. We denote Downsampling as follows:
[sampler(Downsampling (x, Ps) = xx|:: Pa]
(2)
where x|:: Px] means sampling at every Ps points and P, is hyperparameter to optimize.
The operator Aligner addresses the issue of patch alignment for LTMss The recent LTMs typically divide sequences of data points into patches for autoregressive forecasting. If the input data length is not an integral multiple the patch size, LTMs may utilize padding with zeros, which introduces low-quality data. Aligner provides several typical methods of padding the sequences, which may increase the quality of input data. We denote one choice of Aligner, Mean, as follows:
T alligne((Mea)) (x) = pad(x x, mean(x))
(3)
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Published as a conference paper at ICLR 2026
where (x, 1) is function that appends to x to make the length reach integral multiple of the patch size.
A.3.2 NORMALIZING TRANSFORMATIONS
The second is normalizing transformations, which adjust the samples' values constraint range. Normalizing transformations ensures a consistent distribution of input samples' values to help LTMs perform stably(haajaa
Das 2018). The common normalization methods include the standard scaler, which scales data to have zero mean and unit variance, and the robust scaler, which is less sensitive outliers. We denote choice of Scaler, STD, as follows:
X
T scaler(STD) (x)
where μ and represent mean and variance of x seperately.
The operator Differencer transforms the data by computing the differences between adjacent points(K wiitkowkki et al., 1992). It effectively removes acute trends and reduces the variability mean and variance over time, making the data more stationary. Such transformations are crucial for time series models that rely linear stationary assumptions. We denote the first-order choice of Differencer as follows:
T (differencer(()(()) (xt - x,- len(,,))((())
The operator Warper applies transformations, such as logarithmic adjustments, to stabilize the vari- ance(Box & Cox 1964). This approach is particularly beneficial for data with skewed distributions or extreme peaks. We denote choice of Warper, LOG, as follows:
(warper(LoOG)())) log(1 (x|) sign(x)
A.3.3 OUTLIER TRANSFORMATIONS
The third is outlier transformations, which resolve outliers or noise to ensure stable input for LTMs. The operator Denoiser aims to reduce noise the data, making trends and sea- sonal patterns more prominent (Brown) 1959). Techniques such as weighted moving averuge and fast Fourier transform filtering can smooth the data. By increasing the signal-to-noise ratio, these methods may improve LTMs' ability to make more accurate predictions. We denote choice of Denoiser, MA, as follows:
P x
T denooisor(MA) x Pw) 1 P
- 1=1
Pw,..,,,enff
where Pw, is a hyperparameter for the window size of the moving average algorithm.
The Imputator operator replaces anomalies with normal values using methods such as linear inter- polation. Outliers can be detected using methods such as k-IQR(Saradjian & Akhoondzadeh 2011). We denote choice of Imputator, IQR, as follows:
Garciia et al. 2021 and
Xx
Xx xMl S Pk IQR(x)
T Impuuator((QR)) (x. Pk) =
(8)
[x xx+1) otherwise
where Pk is hyperparameter for severity adjustment.
The Clipper operator addresses abnormal data shifts by utilizing the IQR bounds of the original data data. It ensures that extreme values are within a reasonable
clip the extensive range, preventing unrealistic predictions. We denote Clipper as follows:
Telipper(& (x, Pk) max(min(x, Q3 + IQR Pk). Q1 IQR Pk)
where QI and Q3 represent the first and third quartiles, and Pk is a hyperparameter for adjustment of clipping range.
The preceding section describes the nine operators currently adopted the TATO framework. As LTMs and transformation methods advance, the TATO framework will also evolve, with LTMs per- forming more diverse tasks and the transformation pipeline comprising a greater number of opera- tors.
16

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Published as a conference paper at ICLR 2026
A.4 DETAILS OF THE PARETO-BANE BASEE RANKING OF PERFORMANCE
A key challenge in evaluating transformation pipelines that no single trial consistently outperforms others across all metrics; averaging all metrics risks giving poorly performing trials favorable rank- ing. To address this, we adopt a two-stage Pareto-based ranking approach (Palakonda & Mallipeddi] 2017) to select the final transformation pipeline.
In the first stage, we filter out underperforming trials using metric subsets computed on all aug- mented samples. Specifically, we construct multiple metric combinations (e.g.. MSE, MAE, MAPE and MSE, MSPE) and retain only those trials that rank among the best on all combinations, forming a Pareto set. This eliminates trials that excel only on specific metrics while performing poorly on others. In practice, we set the filtering threshold to exclude the worst-perforning trials, balancing risk reduction with computational efficiency.
In the second stage, we rank the remaining trials using only the original (non-augmented) samples. The final ranking is computed as weighted sum of all metric ranks, with MSE assigned the high- est weight (3), followed by MAE (2), and other error metrics each receiving a weight of 1. This weighting scheme reflects mainstream practice in time series forecasting while remaining adjustable to downstream task requirements. The top-ranked transformation pipeline is then applied to test data for FrozenForecasting.
A.5 DETAILED RESULTS
Table 5 provides detailed results of MSE and MAE for predictions across various models and tasks using TATO. The prediction lengths range from 24 to 192, covering both shorter and longer fore- casting horizons. Generally, the error metrics tend to increase with longer prediction lengths, likely due to the increased uncertainty and complexity in capturing long-term dependencies in the data.
Table 5: Prediction results using TATO with various models and datasets.
Models
TiSer-UTSD
Timer-LOTTAA
Moirai-small
Moirai-base
Moirai-large
Chronos-tiny
Error Metric
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
24
0.2521
0.3850
0,2485
0.3724
0,2615
0,3922
0.2617
0.3893
0.2431
0.3724
0.2716
0.3930
ETTh
48
0,3469
0.4511
0.4671
0.5289
0.3707
0.4659
0.3783
0.4658
0,3526
0.4488
0.3922
0.4788
96
0.4567
0.5177
0.4782
0.5254
0,4801
0,5274
0,5305
0.5620
0.4650
0.5173
0.4989
0,5353
192
0,5048
0.5509
0.5578
0.5773
0.6715
0.6322
0.6306
0.6155
0,5956
0,5910
0.6214
0.6075
24
0.2121
0.3505
0.2187
0.3467
0,3803
0.4767
0.3320
0.4349
0.2683
0.3937
0.2446
0.3640
48
0,2822
0.4113
0,3285
0.4322
0.4225
0.5057
0,3927
0.4820
0,3230
0.4379
0,3483
0 0.4482
ETTh2
96
0.3729
0.4762
0,3979
0.5022
0.4874
0,5466
0.4600
0,5315
0.4887
0.4724
0.5323
192
0.4140
0.5120
0.4274
0.5328
0,5862
0.6086
0.6303
0.6361
0.4920
0,5564
0.6536
0.6559
24
0.1179
0.2501
0.0884
0.2114
0.1038
0.2294
0.1014
0.2276
0.0989
0.2253
0.0993
0.2239
ETTml
48
0.2386
0.3654
0.1538
0.2912
0,1619
0,3021
0.1566
0.2943
0.1576
0.2947
0.1823
0.3153
96
0,3399
0.4374
0.2430
0.3716
0.2664
0,3909
0.2739
0.3949
0,2626
0,3860
0,2805
0.3965
192
0.4595
0.5110
0,3903
0.4779
0,3713
0.4645
0,3824
0.4744
0.3821
0.4719
0.4207
0.4858
24 0.1968
0.2861
0.1049
0.2068
0,1120
0,1973
0.1052
0. 0.1955
0.0838
0.11
0.0925
0.1795
ETTm2
48
0.4643
0.5114
0.2150
0.3246
0.2379
0.3297
0.2520
0.3407
0.2467
0.3215
0.2251
0.3219
96
0,5246
0.5652
0,3043
0.4125
0.2639
0.3891
0,2949
0.4014
0.2476
0,3665
0.2964
0.3937
192
0.5617
0.6008
0.3784
0.4642
0.3357
0.4457
0.3526
0.4532
0.3329
0.4356
0.4236
0.4840
24
0.1219
0.2442
0,1208
0.2451
0.4794
0,5175
0.4256
0.4826
0.4355
0.4922
0.1927
0,3060
Electricity
48
0.1560
0.2741
0.17
0.2762
0.5375
0.5507
0.4449
0.4954
0,5173
0.5392
0.2471
0.3470
96
0.1913
0.3014
0,1867
0.3039
0.6047
0,5816
0,4415
0.4970
0.5434
0.5549
0.2985
0.3821
192
0.2224
0.3237
0.2227
0.3289
0.5940
0.5765
0.4615
0.5047
0,5438
0.5537
0.3481
0.4165
24
0.0784
0.2134
0.0732
0.2013
0.0825
0,2139
0.0775
0.2086
0.0720
0. 1976
0.0705
0.1962
Exchange
48
0.1509
0.2901
0.1450
0.2849
0.1676
0,3137
0.1502
0,2870
0.1640
0.3025
0.1383
0.2752
96
0.3634
0.439
0.3027
0.4098
0,3160
0.4109
0.2909
0.3986
0,3099
0.4087
0.2929
0.3962
192
0.6367
0.6191
0.6314
0.5948
0,5525
0,5821
0.5094
0.5401
0,5080
0,5604
0.5099
0,5488
24
0.0557
0.1307
0.0570
0.1335
0,3166
0.4608
0.2860
0.4302
0.2427
0.3675
0.0644
0.1370
Traffic
48
0.0586
0.1338
0.0597
0.1371
0,3317
0.4656
0.2806
0.4136
0.2640
0.3858
0.0739
0.1490
96
0.0609
0.1371
0.0621
0.1399
0.3574
0.4786
0.2638
0.3983
0,2592
0,3800
0.0798
0.1601
192
0.0606
0.1394
0.0621
0.1430
0,3249
0.4602
0.2670
0.4027
0.2419
0.3733
0.0893
0,1753
24
0.2650
0.3321
0.2227
0.2954
0.2207
0.2981
0.2259
0.2984
0.2428
0,3036
0.2250
0.3033
Weather
48
0.5237
0.4856
0.6100
0.5638
0.3969
0.4306
0.3841
0.4140
0,3986
0.4281
0.4225
0.4456
96
0.9170
0.6769
0.6858
0.5922
0.6100
0.5666
0,5982
0,5367
0.6146
0.5540
0.8986
0.6746
192
0.7136
0.6214
0.8408
0.6857
0.7501
0.6302
0.7764
0.6259
0.7870
0.6415
0.8459
0.6745
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Published as a conference paper at ICLR 2026
A.6 ON DIFFERENT PERSPECTIVES
Table 6 provides additional results from Section 4.3 We analyze the statistics of MSE and MAE, including Mean, Median, STD, and IQR, calculate % Promotions across various scenarios. From model perspective, TATO consistently enhances performance across all models. In particular, Moirai shows that our method improves forecasting performance more effectively with larger models.
Table 6: Results of prediction using TATO from different perspectives. Percentage improvements (% Promotion) are grouped and averaged by model, data, and task.
% Promotion of Error Metric Statisties
MSE Mean % Promotion
MAE Mean
MSE Mediun % Promotion
MAE Median Promottin
MSD % Prumootinn
MAE STD % Promotion
IQR Promotion
IQR
Promotion
Prrmmotion
Timer-U/TSD Tmme-LOTSA
6,00%
4 .100
-1. 1.44%%
0.90%
6,005
8.50%
5.10%
6,00%
24,80%
15 70%
17.10%
13 4.40%
31.30%
23.10%
16.905
14.30%
Model
Moirni-uaall
0.0%
5.20%
Moirai-large
18.10%
Chronos-tiy
14,50%
7.00%
10.30%
0.40%
23.30%
9.10%
11.70%
5.90%
ETTh1
3,40%
1.80%
4.00%
1 50%
2,00%
1.00%
5.60%
2.60%
ETTh2
6.40%6
3,30%
4,8015
2,80%
9.30%
6,50%
12,00%
9000%
ETTml
15.90%
8.80%
12,40%5
90%
30%
12,80%5
15.50%
10.50%
ETTm2
25.70%
5..00
28.7016
27.20%
18.
Electniity
Exchange
10%
19
10%
Trafiic
12.40%
7.70%
13.10%
7 3.30%
4.30%
8.00%
12.10%
6.70%
Weather
1,50%
1.10%
-1.30%
0 40%
10%
1,50%
00,0%%
-0,20%
24
21.60%
13 4045
20.10%
12.50%
16,00%
14,00%
21. 31,80%
14. 3033
Task
48
14,00%
8 3.90%
13,6055
7 7.90%
30.00%
8.90%
12.305
7.30%
96
11.70%
9.10%
From data perspective, the improvement is most pronounced in the Exchange and ETTm2 datasets, exceeding 20%. However, the gains on the Weather dataset are relatively small, likely due to the in- herent variability of weather data. From task perspective, TATO demonstrates stable improvements across all prediction lengths, with shorter ones showing greater benefits. Additionally, the %T Promo- tions for STD and IQR suggest a reduction in variance, highlighting TATO's ability to deliver more accurate and robust forecasts.
A.7 MORE SCALABILITY EXPERIMENTS
We conduct a series of scaling experiments on different "number of samples", "number trials", and "more diverse horizons". Compared to the experiments in our main text, we take more samples, more trials,and longer forecasting horizons into consideration. We also use Time Cost and Loss Improvements evaluate the scaling process. For simplicity, all efficiency tests are conducted on Timer-LOTSA, and performance improvements are calculated on ETThl and ETTml. Table 7 presents performance across different sample sizes and transformation trials. Table S presents the performance across different prediction horizons.
In terms efficiency, the time cost shows a near-linear relationship with the number of samples, trend that holds across different transformation trials. In terms of performance, TATO demonstrates robust results when the number of samples (or transformation trials) exceeds 500. The performance varies across horizons, and the required time is acceptable, further attesting to TATO's broad appli- cability.
Table Scaling performance under different sample sizes and transformation trials. The results are averaged by horizons (24, 48, 96, 192, 336. 720).
Trials
Samples
Metric
500
1000
1500
500
1000
1500
Time Cost
428.3
491.1
569.4
428.3
478.9
503.4
MSE Imp.
21.2
22.3 %
20.4 %
21.2
21.8
20.6
MAE Imp.
12.6 %
13.2 %
12.3 %
12.6 %
12.6 %
12.1 %
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Published as a conference paper at ICLR 2026
Table 8: Scaling performance across different prediction horizons. The improvement metric is
defined as (MSE improvement percentage / MAE improvement percentage).
Dataset
Mean Timels)
Average Imp
Horizon 24
Horizon 48
Horizon 96
Horizon 192
Horizon 336
Horizon 720
ETThi
9.5%
19.4%/11,4
-17.7%/-11.19 1%
0.0%//0.%
0.0%//b.09
8.1%/44.39
28.9%/11.3%%
ETTml
445.5
23.3%
67.3%
53.5%//32.69
39.6%/22.88%
18.3%\/9.9%
-0.5%.10.8%
4,092/2.66
Exchange 408.3
35.5%/31..%
8944%/67.76
81,5%/58. 1%
66. 156/44.25
35.0%/23.14 1%8
-11,9%./1.35
-26. 19//4.44 8%%
Traffic
3.2%//166%
19


Reviews:
[{"review_id": "0RanAOzjoa", "reviewer": "Reviewer_69Mi", "summary": "The paper is flawed due to deficient writing, a lack of crucial experimental comparisons against a fine-tuned LTM and other established time-series methods, and the absence of a required quantitative analysis on computational overhead.", "questions": "No", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "The authors provide sufficient ablation studies and comparisons, which enhance the credibility of the findings.", "weaknesses": "1. The writing is deficient, like the transition from paragraph in line 55 to 57 feels abrupt; Section 2.3 is a seemingly extraneous point, confusing the reader and disrupting the narrative flow; Section 3.1 is repetitive because the same content has been discussed before; In figure 2, it seems like the order of 3 types of operators is reorderable, but it's said in the text that they are fixed, it's confusing which ones are reorderable; In Section 3.2.2, post-process operators are mentioned, but lacks a explaination of what exactly they are; Section 4.2 is repetitive too.\n2. You have verified that your method yields better results than the baseline (without TATO) on a specific dataset. However, how does it compare to LTM that has been fine-tuned on the same dataset? A sole comparison between having TATO and not having TATO is insufficient to prove the efficacy of your method, as direct fine-tuning on the LTM might achieve superior results.\nFurthermore, regarding the SOTA method, how much computational resource saving does it actually provide? You need to compare the overhead and computational resources required by your method against those needed for fine-tuning the LTM, rather than merely stating how little time your method requires.\n3. As I understand it, TATO is merely a combination of several common transformation methods. Given this, there are many other established methods for processing time-series data—is your approach superior to them?\nIf your claimed advantage is superior generalization, then you must also provide evidence that other methods perform worse than yours in terms of generalization. It is not enough to simply state that these other methods are designed for \"specific problems\"; this claim needs to be supported by experiments.\n4. I don't understand the point of section 4.5. What specific benefit of your method are you trying to demonstrate with this experiment? Furthermore, why are only two models used in this experiment? What about the others? \n\"First, we finetune Timer-UTSD over all eight datasets above to get a single LTM that works well on all of them.\" How well does it work? Lacks experimental data.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "cDulVrjszL", "reviewer": "Reviewer_YMH3", "summary": "The paper proposes TATO, a framework to adapt data rather than models for time series forecasting. Instead of fine tuning large time series models (LTMs), TATO keeps them frozen and learns optimal preprocessing transformations to improve performance across domains. The method searches transformation pipelines including context slicing, scale normalization, and outlier correction using hyperparameter optimization and a two stage Pareto based ranking for robustness. Experiments on LTMs (e.g., Timer, Moirai, Chronos) and datasets (ETT, Electricity, Exchange, Traffic, Weather) show consistent improvements. This work introduces a practical FrozenForecasting paradigm for universal, efficient forecasting.", "questions": "1. How sensitive is the optimization process to the size and diversity of the sampled data? Would using fewer or noisier samples significantly affect the quality of the selected transformation pipeline?\n\n2. Could the authors clarify how TATO generalizes to multivariate or irregularly sampled time series, where contextual or scale transformations may interact differently?\n\n3. How does TATO compare empirically or conceptually with recent test time adaptation methods TAFAS, PETSA, DynaTTA?\n\n4. Are there any insights into which specific transformations contribute most to improving generalization across domains?\n\n5. Could TATO be combined with lightweight fine tuning or LoRA style adaptation to further improve performance while maintaining efficiency, and if so, what trade offs might emerge?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "The paper presents an original paradigm that shifts the focus from model adaptation to data adaptation, introducing the concept of FrozenForecasting for large time series models. The proposed TATO framework is well designed, combining hyperparameter optimization and Pareto based ranking to ensure both robustness and efficiency. The methodology is clearly described with experiments across several state of the art LTMs and diverse datasets. The work provides some evidence of performance improvement with minimal computational overhead, highlighting its practicality for real world applications.", "weaknesses": "While the idea of adapting data instead of models is novel, the paper could benefit from a deeper theoretical justification of why certain transformations consistently enhance generalization across domains. The search space for transformation pipelines, though compact, may still be computationally demanding for larger datasets or longer horizons, and scalability analyses beyond 500 samples are limited. Some test-time adaptation methods for time-series forecasting were completely ignored by the authors, such as TAFAS (Kim et al. (2025).) [1], PETSA (Medeiros et al. (2025)) [2], DynaTTA (Grover & Etemad. (2025)) [3], I would suggest the authors to review these additional methods for the related works to strong contribution of the proposed TATO, and also compare with some of the methods if possible, because in the main comparisson the paper is missing comparisson with other methods. Additionally, the ablation results could include more quantitative discussion on how each operator contributes to generalization rather than overall MSE reduction.\n\n\n\n[1] HyunGi Kim, Siwon Kim, Jisoo Mok, and Sungroh Yoon. Battling the non-stationarity in time\nseries forecasting via test-time adaptation. In AAAI, pp. 17868–17876, 2025. URL https:\n//doi.org/10.1609/aaai.v39i17.33965.\n\n[2] Heitor Medeiros, Hossein Sharifi-Noghabi, Gabriel Oliveira, and Saghar Irandoust. Accurate\nparameter-efficient test-time adaptation for time series forecasting. In Second Workshop on Test-\nTime Adaptation: Putting Updates to the Test!, 06 2025. doi: 10.48550/arXiv.2506.23424.\n\n[3] Shivam Grover and Ali Etemad. Shift-aware test time adaptation and benchmarking for time-series\nforecasting. In Second Workshop on Test-Time Adaptation: Putting Updates to the Test! at ICML\n2025, 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Pbi81vYFNe", "reviewer": "Reviewer_kQ8y", "summary": "This paper introduces an interesting paradigm that aligns time series data with a pretrained large time series model through transformation pipelines. By automatically selecting feasible pipelines via a two-stage ranking process, the proposed framework aims to enhance forecasting performance on target domains without requiring fine-tuning of the backbone models.", "questions": "Please refer to the weaknesses part.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The idea of leveraging transformation pipelines to align time series data with forecasting models is novel and promising, as it improves domain-adaptive forecasting performance while keeping the backbone models fixed.\n- The proposed pipeline optimization process is efficient, typically requiring less than two minutes in most cases.\n- Experimental results demonstrate that the proposed framework effectively boosts the performance of various backbone models on widely used benchmark datasets.", "weaknesses": "- Clarity and presentation:\n  - The framework description lacks sufficient detail. For instance, how are individual trials sampled from the pipeline space? Providing pseudocode or an algorithmic overview would greatly improve readability and reproducibility.\n  - It is mentioned that only a portion of the training samples are used in the TATO framework, but the selection strategy for these samples is unclear. A detailed explanation of how these samples are chosen from the full training set would strengthen the paper.\n- Experimental analysis:\n  - Table 2 reports runtime comparisons across different configurations, but it would be helpful to also include forecasting performance metrics in the same table to illustrate the efficiency–effectiveness tradeoff of TATO.\n  - Although TATO is primarily proposed for large time series models, it would be interesting to investigate whether conventional models such as PatchTST can also benefit from TATO under cross-domain forecasting scenarios.\n  - Section 4.5 compares LTMs tuned over eight datasets against TATO. However, it would be insightful to include results where the model is tuned on a *single* dataset (e.g., tuning Timer on ETTh1 with a comparable computational budget in terms of training time or samples) to better contextualize TATO’s performance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "p5ZLeeWprB", "reviewer": "Reviewer_8T1Q", "summary": "This paper introduces Time-Series Adaptive Transformation Optimization (TATO), a framework designed to enhance the generalizability of frozen large time series models (LTMs) across diverse domains. Instead of retraining or creating new models, TATO automatically searches for empirically optimal transformation pipelines—comprising context slicing, scale normalization, and outlier correction—to adapt LTMs to new data distributions. Through a robust two-stage ranking mechanism, TATO ensures consistent performance across metrics and achieves substantial forecasting improvements, reducing MSE by up to 68.4% on standard benchmarks, all within a practical optimization time of under two minutes.", "questions": "1.\tCan TATO be applied to other time series tasks, such as imputation?\n2.\tWhat is the relationship between FrozenForecasting and Data2Model? It appears that the authors use them interchangeably.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "1.\tThis paper stands on a data manipulation perspective to improve the performance of time series forecasting, which is kind of novel. \n2.\tTATO is evaluated on both time series foundation models and LLM-based models to demonstrate its universal applicability across various model architectures. \n3.\tPerformance gain brought by TATO is significant, reaching up to 68.4%.", "weaknesses": "1.\tIt is hard to understand what transformations are applied to the time series in Figure 1.\n2.\tTATO’s effectiveness heavily relies on a fixed set of nine transformation operators. The search space is manually defined, which may limit adaptability to unseen or highly domain-specific data types.\n3.\tThe framework is presented primarily as an empirical optimization procedure, with little theoretical grounding on why certain transformations generalize well across domains or how the two-stage Pareto ranking ensures robustness.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)