Repurposing Foundation Model for Generalizable Medical Time Series Classification
Authors: Nan Huang
Haishuai Wang
Zihuai He
Marinka Zitnik
Xiang Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Medical Time Seris
Classification
Time Series Foundation Model
Primary area: learning on time series and dynamical systems
TL;DR: FORMED repurposes pre-trained time series models for medical classification, achieving 35% F1-score improvement through lightweight adaptation across diverse datasets.

Abstract: Medical time series (MedTS) classification suffers from poor generalizability
in real-world deployment due to inter- and intra-dataset heterogeneity, such as varying
numbers of channels, signal lengths, task definitions, and patient characteristics.
% implicit patient characteristics, variable channel configurations, time series lengths, and diagnostic tasks.
To address this, we propose FORMED, a novel framework for repurposing a backbone foundation model, pre-trained on generic time series, to enable highly generalizable MedTS classification on unseen datasets.
FORMED combines the backbone with a novel classifier comprising two components: (1) task-specific channel embeddings and label queries, dynamically sized to match any number of channels and target classes, and (2) a shared decoding attention layer, jointly trained across datasets to capture medical domain knowledge through task-agnostic feature-query interactions. After repurposing, FORMED achieves seamless adaptation to unseen MedTS datasets through lightweight label query training (0.1\% of parameters), eliminating the need for full fine-tuning or architectural redesign.
We evaluate FORMED on 5 diverse MedTS datasets, benchmarking against 11 Task-Specific Models (TSM) and 4 Task-Specific Adaptation (TSA) methods. Our results demonstrate FORMED's dominant performance, achieving up to 35\% absolute improvement in F1-score (on ADFTD dataset) over specialized baselines.
By decoupling domain-invariant representation learning from task-specific adaptation, FORMED establishes a scalable and resource-efficient paradigm for foundation model repurposing in healthcare. This approach prioritizes clinical adaptability over rigid task-centric design, offering a practical pathway for real-world implementation.

Published as a conference paper at ICLR 2026
REPURPOSING FOUNDATION MODEL FOR GENERALIZ- ABLE MEDICAL TIME SERIES CLASSIFICATION
Nan Huang
Haishuai Wang
University of North Carolina at Charlotte
Zhejiang University
NC, US nhuang1 wwwarlotte.ee...ooooooooooe
Zhejjang, China ha haishuai, wwt.ei.www@@eeee..oo
Zihuai He
Marinka Zitnik
Stanford University Stanford, CA, US zihuai @ettanforddd..oommo edu
Harvard University
Boston, MA, US
ma marinti marinka@ hms hawvard. edd
Xiang Zhang
University of North Carolina Charlotte NC, US xi ang. zhang@chhallottee.ed
ABSTRACT
Medical time series (MedTS) classification suffers from poor generalizability in real-world deployment due to inter- and intra-dataset heterogeneity. such as varying numbers of channels, signal lengths, task definitions, and patient characteristics. To address this, we propose FORMED, a novel framework for repurposing backbone foundation model, pre-trained on generic time series, enable highly generalizable MedTS classification on unseen datasets. FORMED combines the backbone with a novel classifier comprising two components: (1) task task-specific channel embeddings and label queries, dynamically sized to match any number of channels and target classes, and (2) a shared decoding attention layer, jointly trained across datasets capture medical domain knowledge through task-agnostic feature-query interac- tions. After repurposing, FORMED achieves seamless adaptation to unseen MedTS datasets through lightweight label query training (0.1% 1% of parameters), climinating the need for full fine-tuning or architectural redesign. We evaluate FORMED on 5 diverse MedTS datasets, benchmarking against Task-Specific Models (TSM) and 4 Task-Specific Adaptation (TSA) methods. Our results demonstrate FORMED's s dominant performance, achieving up 35% absolute improvement in F1-score (on ADFTD dataset) over specialized baselines. Further analysis reveals consis- tent generalization across varying channel configurations, time series lengths, and clinical tasks, which are key challenges in real-words deployment. By decoupling domain-invariant representation learning from task-speeific adaptation, FORMED establishes a scalable and resource-efficien paradigm for foundation model re- purposing in healthcare. This approach prioritizes clinical adaptability over rigid task-centric design, offering practical pathway for real-world implementation. Code is available at https: / /qi giihhub... com.DLAmmealta. FORMED....
INTRODUCTION
Medical time series (MedTS) classification, such electroencephalograass (EEG) and electro- cardiograms (ECG), critical for diagnosing a wide spectrum medical conditions, including Alzheimer's 's Disease (AD; Jeong (2004)), Parkinson's 's Disease (PD; Aljalal et al. (2022b:a)), and heart Arrhythmia (Jin et al., 2024b). Despite significant advancements in developing deep learning models for these tasks, their effective generalization across diverse datasets, sometimes among individ-

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Figure 1: Paradigms of building models for different MedTS classification tasks. Task-Specific Model (TSM): Traditional classification models are designed for specific input shape and output classes, thus require retraining from scratch for each new dataset. Task-Specifie Adaptation (TSA): By using a pre-trained and fixed backbone foundation models, the adaptation to new datasets requires training fewer parameters for each dataset, such as pre- and post-backbone adapters, which makes the combined model no longer applicable to other tasks, lacking generalization across tasks, and more prone to overfitting. Generalizable Adaptation (GA): Generalizable adaptation a post-backbone adaptation module that is shared across tasks of different datasets, which carries domain knowledge and transferable to unseen datasets with training of lightweight task-specific parameters, offering both generalizability and robustness against overfitting.
ual patients within the same dataset, remains a significant hurdle. This limitation critically obstructs the translation of advanced predictive algorithms into reliable real-world clinical applications.
Several unique challenges inherent to MedTS data compound the poor generalizability. Firstly, inter-dataset heterogeneity arises from variations in physiological data domains, data collection equipment, and experimental protocols, leading to differences in the number of channels, sample durations, sampling rates, and diagnostic targets (Ganapathy et al., 2018). Secondly, intra-dataset heterogeneity persists even within single datasets, with variations occurring across recording times, experimental sessions, and, most importantly, among patients due to unique physiological character- istics (Wang et al., 2024b; Ganapathy et al., 2018). This often leads to models overfitting to training data and failing to generalize to unseen patients. Thirdly, data insufficiency a persistent issue: the high cost of data collection and privacy concerns often result in small MedTS datasets (Kaushik et al., 2020), making it difficult to train robust models capable of addressing the aforementioned heterogeneity (Ganapathy et al., 2018).
Previous attempts to tackle these issues, such as employing Task- Specifici Adaptation (TSA; see Figure 1) in models like Yang et al. (2023), have shown limited success. These methods may unintentionally focus on extracting features relevant only to the initial training task, thereby failing to generalize effectively new datasets different medical conditions, as evidenced by marginal or even negative performance gains through pre-training. While on the other hand, recent advancements in foundation models for time series offer a promising avenue. Despite their predominant focus on forecasting tasks ( (Ye et al., 2024; Wen et al., 2022), they demonstrate the ability to learn generic representations of time series data (Liang et al., 2024), thanks to pre-training on large-scale general time series data. This can be beneficial for MedTS classification tasks as well. Our pilot study indicates that directly adapting these models for MedTS classification is better than TSA models trained from scratch, but still fall short in capturing the intricate patterns necessary for specific diagnostic tasks when compared to established Task -ppecffcc Models (TSMs; see Figure 1). This is primarily due to their lack of ability to capture the task-agnostic domain knowledge, which is crucial for generalization across datasets.
To address these limitations, this paper introduces FORMED (Foundation model Repurposed for Medical time series classification), a novel approach designed to repurpose foundation models for MedTS classification. FORMED aims to achieve Generalizable Adaptation (GA; see Figure I). This is achieved by utilizing a pre-trained foundation model as its backbone to capture generic temporal features, and integrating novel classifier design to handle MedTS heterogeneity, by architecturally separating medical domain knowledge from task-specific knowledge. This allows the model to effectively learn and utilize both task-agnostic and task-specific features, enabling
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seamless handling of datasets with arbitrary channel configurations, dynamic time series lengths. and diverse diagnostic targets across multiple tasks. Therefore, FORMED enables the backbone model to effectively leverage the commonalities across datasets, while also being flexible enough to adapt to the unique characteristics of each dataset, thus achieving generalization across datasets and tasks.
To facilitate this research, we adopt the comprehensive MedTS cohort curated by (Wang al., 2024b) as repurposing cohort, which comprises five Med'SS datasets (two ECG and three EEG). This collection includes approximately 340,000 samples (90 million time-points) in total. These datasets exhibit diverse channel configurations (ranging from 12 to 33 channels), varied diagnostic tasks (from binary neurological to 5-class cardiovascular classification), and differing dataset sizes. This provides different levels of difficulties (both inter- and intra- intra-dataset) for the model to learn from, and serves as a robust training and evaluation platform for the proposed method.
FORMED is strongly supported by empirical results on two aspects: First, for datasets partially included in the repurposing cohort, FORMED achieves state-of-the-ard level performance on unseen patients, outperforming 15 TSA and TSM models across all datasets. Second, for completely new datasets not included in the cohort, FORMED can efficiently adapted by updating only a small proportion of parameters while outperforming the baseline TSA model, and shows robust adapt-time scaling performance with the amount of trainable parameters. This demonstrates the model's 's ability to generalize across datasets and tasks, and its potential for real-worls applications in healthcare.
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RELATED WORK
2.1 FOUNDATION MODELS FOR GENERAL TIME SERIES
While recent models like MOMENT (Goswami et al., 2024) and UniTS (Gao et al., 2024) incor- porate classification objectives, the majority of foundation model remains heavily concentrated on generative forecasting tasks (Liang et al., 2024). Given their success in forecasting, re- re-purposing these forecasting-orice oriented models for MedTS classification is a tempting prospect, yet it presents significant theoretical and practical challenges. These models often have major limitations, such as an inherent design for univariate time series in a channel -ineeeeddeeeeeeeent fashion (Nie et al., 2022), and requiring TSAs that prevent them from being directly applicable to MedTS classification tasks (Cao al., 2024: Sun al., 2024: Chang et al., 2023). Given that medical time series are typically multi-variate a critical aspect of our repurposing framework is to effectively integrate the information from multichannel features extracted by these backbones.
For instance, models like Time-LLM (Jin et al., 2024a), UniTime (Liu et al., 2024) and GPT4TS (Zhou et al., 2023a) use large language models as backbones. Consequently, they naturally handle time series data in univariate manner, lacking the ability to integrate information across multiple channels crucial for MedTS classification. Moreover, consistent with the findings of Tan et al. (2024), empirical observations show that LLM-based time series foundation models do not always achieve optimal performance, even on general time series datasets. Similarly, while TimeGPT (Garza et al., 2024) and TimesFM (Das et al., 2024) are pre-trained on large scale time series data, they typically operate under channel-indee -independence assumption, treating co-evolving multivariate time series data as a collection of independent univariate series. This shares the same limitation for direct application to multichannel MedTS. Our proposed repurposing framework is specifically designed to address this by incorporating mechanisms to accommodate the multichannel nature of MedTS data, allowing for effective integration of information across channels.
UniTS (Gao et al., 2024) stands out as capable of handling multivariate time series data and has been trained on multiple task domains including classification. However, its scale and design often necessitate fine-tuning the entire model or employing prompt learning for optimal performance. This approach is both computationally expensive (see Figure I) and data-greedy due to the vast number of parameters to tune, rendering it less suitable for often small-scale MedTS datasets.
Despite their efforts and successes, current foundation models require significant adaptations. Thus, a key challenge, which FORMED directly addresses, is the adaptation of these powerful but often channel-independen demands of MedTS. This is achieved by integrating dedicated architectural components to address the complexities and multichannel nature of MedTSS While all the afonementioned models represent
prrecasttin-focusee models to the multichannel classification
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potential backbones for our repurposing framework, due to resource limitations and the primary goal to validate the efficacy of our proposed framework, we selected the advanced TimesFM (Das et al., 2024) as our backbone model for this study for its outstanding zero-shot forecasting performance.
2.2 ADAPTATION OF FOUNDATION MODELS FOR MEDTS CLASSIFICATION
General-purposs. foundation models typically require specific techniques to be effectively adapted for down- stream tasks. Common approaches include prompting, fine-tuning, re- programming, and we propose re- purposing as novel approach, each with its own advantages and limita- tions as summarized Table We will focus here on the distinction between re-programming and re-purposing, as this differentiation is central to our proposed approach for MedTS classification. Further discussion are in Section A.
Table 1: Comparison of adaptation techniques of time series foundation models. Column meanings are in Section A.
Adaptation
Data Efficiency
New Task Type Generalizability
Prompting Fine teenitunng
Re-programming
Re-purposing
Re- Re-programming often involves reusing pre-trained model's backbone (e.g., its Transformer layers) without altering its internal weights (Jin et al., 2024a; Chang et al., 2023; Sun et al., 2024; Zhou et al., 2023a), but wrapping it with new input adapters and task-specific output heads (as illustrated by the TSA approach in Figure 1). While this can adapt a model to a new data domain or task type, a significant drawback is that the resulting model often loses its general-purpose nature. Both the input adapters and task heads become highly specialized, and cannot be reused future datasets with different configurations, hindering the model's ability generalize across different lasks, datasets, or to handle the inherent heterogeneity within MedTS (Tan et al., 2024).
Re-purposing, introduced in this work with our FORMED framework, takes different philo- sophical approach. It aims to adapt a pre-trained foundation model (often one excelling in tasks like forecasting) to new class of tasks-in our case, MedTS classification. This is achieved with thoughtful modifications, particularly in how task-specific knowledge is integrated, while striving maintain the model's core learned representations. The goal is for the repurposed model serve as robust and generalizable tool within the target domain (MedTSS classification), capable of be- ing efficiently applied to new datasets and diagnostic challenges. Its emphasis on generalizability. data-efficiency, and leveraging domain insights makes re-purposing particularly suitable for adapting powerful, channel-independee time series foundation models to the complexities of multichannel MedTS classification, and creating new foundation model for the field.
2.3 FORECASTING VERSUS CLASSIFICATION IN TIME SERIES
The fundamental differences between time series forecasting and classification are key to under- standing the challenges in adapting existing foundation models. Forecasting typically involves predicting future sequence values within the same domain the input, e.g., sequence - sequence mapping (Lim & Zohren, 2021; Wang et al., 2024a), often by extrapolating patterns within individual channels. In contrast, classification maps input sequence sequence - category mapping (Ali et al., 2019). This frequently requires synthesizing complex patterns across multiple interacting channels to derive diagnostic outcome, e.g., diagnosing disease from multichannel EEG (Wan et al., 2023) - a process inherently different from predicting future signal values. This intrinsic divergence in objectives and the nature of data interpretation means that adapting a forecasting foundation model for MedTS classification is NOT simple modification of the output layer. demands more comprehensive re- re-purposing strategy, such as our FORMED framework, designed to bridge these task-specific requirements and effectively handle the complexities of multi-variate medical data.
distinct categorical label, e.g.,
PROBLEM STATEMENT
Foundation models, pre-trained on diverse forecasting tasks, have demonstrated strong capability to capture general time series patterns. Medical waveform data (e.g., EEG, ECG) shares the continuous,
Athhoug the model structure is fixed and still applicable to other datasets and tasks, the engineered or learned prompts can be task-specifie.
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Figure 2: The staee process of adapting time series foundation model for MedTSS classification tasks. 1) Pre-training is already done on diverse general time series datasets with forecasting tasks. 2) Repurposing the foundation model involves changing the forecasting head to classification head, while keeping the rest of the model fixed, and the new model then trained on a cohort of MedTS datasets to capture domain knowledge in MedTS. 3) Adapting the repurposed model to the new Med'SS datasets, where only the minimal task-specific parameters are trained, leveraging the previously learned domain knowledge from the repurposed model.
high- fre.uenuuunyy characteristics of the general time series used pre-traennnt pre-training. While sparse, irregular Electronic Health Records (EHR) also "medical time series," " they require fundamentally different tokenization strategies outside the scope of this waveform-comee centric study. Even with waveform data, classification
transforming pre-trained general time series foundation models into general-purper models, especially for Med'TS, is not trivial. This section formally defines the problem and the core concepts underpinning our proposed two-stage adaptation process: repurposing and adapting.
Definition 3.1. Repurposing: The process of changing the objective of a pre-trained foundation class of tasks for which it not originally trained. This involves introducing and training a relatively small, adaptable output network while keeping the pre-trained backbone fixed.
model
Let the original pre-trained model consists of backbone : RLXD for extracting features from a univariate time series of length T into I patched tokens of dimension D, and an original task head (e.g., for forecasting, IR LxD + RR We leverage the frozen backbone f for representation learning. For multivariate MedTS input E RCXT with C channels, the backbone applied channel-wise extract features:
f RCCT + RCLXD e +(X)= = [f(X.:)
We then introduce a novel, trainable classification head hgg This head is designed to be adaptable to specific task characteristics, such the number of input channels C' and the number of output classes K, through learnable task-specific parameters: Channel Embedding E RCD and Label Queries Q RR RxDD The mapping becomes:
hel helq,E RCXLDD + AK (he o f)
RC A
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EK d. 1 is the probability simplex for classes.
During the repurposing stage, he containing shared parameter along with collections of task- specific embeddings E { and Q = { are trained across cohort of diverse MedTS datasets
Deel = [DMed). }. The objective to learn domain knowledge for MedTS classification within This translates to minimizing the cross-entropy loss Ler:
0" E arg min E, (X,,,.)) [ hholq..e. (f(X))) . 8,E.Q
Definition 3.2. Adapting: The process of applying the repurposed model (with fixed 6* and frozen f) to new, unseen MedTS datasets or tasks. This involves learning only a minimal set new task-specific parameters (new E' and Q') for the new dataset.
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For new dataset DNew (with potentially different C' channels, T' time steps, and K' classes), the pre- pre-trained backbone and the shared parameters 8 of the classifier hg- remain frozen. Only new Channel Embeddings E' E x and Label Queries Q' E TRK'x are initialized and trained:
E. Q' arg min E(X) [< (ho-la (f(X')), ,)))
(4)
This two-stage process (illustrated in Figure 2) allows the model learn general MedT' domain knowledge and efficiently specialize to new tasks with minimal new data and computation.
4 MODEL ARCHITECTURE
Our FORMED framework repurposes a pre-trained time series foundation model to serve as versatile MedTS classification tool. It comprises two main parts: frozen backbone feature extractor and our novel attention-basse classifier designed for adaptability and generalizability.
4.1
BACKBONE FEATURE EXTRACTOR FOR VARIABLE LENGTHI TIME SERIES
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Figure 3: The architecture of the proposed model repurposing and adapting. The backbone founda- tion model acts as feature extractor and remains frozen all the time. The Channel Embeddings (CEs) and Label Queries (LQs) are task-specific parameters that are learned during both repurposing and adapting, and new ones will be created and learned if encountering new datasets. The Shared Decoding Attention (SDA) is a shared Transformer decoder layer that captures the interaction between all the features and classes, which once get trained on curated MedTS datasets DMed during repurposing, will be fixed and reused when adapting denotes broadcast addition.
future datasets and tasks DNow The ()
We employ a powerful pre-trained time series foundation model as the backbone feature extractor. For this work, selected TimesFM (Das et al., 2024) due to its demonstrated ability capture complex temporal patterns from variable-length time series, pre-trained on a vast and diverse corpus of general time series data. The backbone's primary role is to transform
input univariate time series æ E IRT into a sequence of rich feature tokens H E R LxD, As defined in Equation (1), for multichannel MedTS data X CCxT this backbone is applied independently each channel, yielding stacked feature tokens H E R LxL The internal architecture of TimesFM (e.g.. its patching mechanism and Transformer layers) is kept frozen throughout our framework and detailed in Section B. The crucial output for our purpose is H. which serves as the input to our novel classifier.
4.2 ATTENTION-BASE BASED CLASSIFIER FOR VARYING CHANNEL AND CLASS
Traditional approaches often use a simple linear layer or convolution layer for classification (Zerveas al., 2021; Yang et al., 2023), which can be restrictive when dealing with the inherent variability of MedTS data (e.g., varying numbers channels, classes, and sequence lengths). To address these unique challenges, propose novel bassdd classifier built upon Transformer decoder

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layer (Vaswani et al., 2017), inspired by advancements in vision tasks (Carion et al., 2020; Meng et al., 2023) but with key modifications tailored for MedTS. This classifier comprises three main components: Channel Embeddings, Label Queries, and Shared Decoding Attention mechanism.
Channel Embeddings (CEs). MedTS are inherently multi-varian and heterogencous, often varying in channel configuration across datasets. To address this specificity of medical data without altering the backbone architecture, we introduce learnable Channel Embeddings E E RCxD This allows the model decouple the spatial topology of the specific medical modality from the generalizable temporal features. For given dataset with C' channels, these embeddings are broadcast-added to the corresponding channel-wise wiie feature tokens (from Equation (I)) produce "channel-award feature tokens E RCCLDD:
(1, Ha.:: He,t: @ Ee.: E (1,2, ................................ ,L,,c (1,2, .C
(5)
These CEs are task-specifie and learned during both repurposing (part of E in Equation (3)) and adapting (E' in Equation (4)).
Label Queries (I.Qs). To handle varying numbers of diagnostic classes (K) per task and provide distinct learnable "anchors" for each class, we use Label Queries Q E RXx Fach row Qu: is a learnable embedding representing the i-th class. These queries actively seek evidence for their respective classes within the channel-aware feature tokens. Like CEs, LQs are task-specific and learned during repurposing and adapting. In practice, employ k learnable queries for each class to capture potentially complex or multiple defining patterns, where & is a hyperparameter determining -sub-patter detectors" for each class. This results in total of K k queries in Q E (KK-)) Each group of k queries corresponding to specific class independently attends to the feature tokens to gather evidence.
the number of distinct "perspectives"
Shared Decoding Attention (SDA). The core of our classifier is the SDA mechanism, a single Trans- former decoder layer whose parameters (0 in Equation (3)) are shared across all datasets the repur- posing cohort and then frozen during adaptation to tasks. The SDA takes all K & Label Queries from Q queries, and the flattened, channel-aware feature tokens Flatten(H) R(C-L)xD as keys and values. It performs multi-head attention followed by an FeedForwarddeetwwrr rdNetwork to produce an initial set of logits yraw E K.
Draw = Feed oreorrwwaae roNetwork Mu (Mult www.eetttttetiinne....ee (n, Featotn.llaaeee///aa FH) Flatten
(6)
Since we have k queries (and thus k raw logits) for each of the K classes, these l logits are then averaged to produce a single, final logit for each class, resulting the final class logits ý E RRK:
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These final logits ý are then used with a softmax function for probability prediction and loss calculation. Critically, the parameters within the MultiHeadAttent FeedF oredForwaddNetwe
and the (collectively 0) are independent the specific number of input channels C, token length L, or the total number of queries K k. This architectural choice forces SDA to learn generalizable interaction patterns while allowing for richer, more nuanced class representations.
EXPERIMENTS
Here we describe the experimental setup, including the datasets chosen to evaluate FORMED against key MedTS challenges, the baselines selected to highlight the advantages of our repurposing framework, and evaluation metrics. Additional training details are included in Section C.
Datasets. To thoroughly evaluate FORMED, we use curated MedTS cohort ( (Wang et al., 2024b) of 5 diverse datasets for the crucial repurposing stage (Figure 2). This cohort, comprising two ECG datasets and three EEG datasets (details in Table 3), provides a breadth of configurations and task types. This enables the learning of robust, generalizable MedTS domain knowledge during repurposing. Critically, these datasets span variety of combinations of channels, sampling rates, sample durations, diagnostic labels, and overall sizes. This inherent diversity is intentional, enabling directly assess FORMEN TORMED's ability to handle inter-dataset heterogeneity during repurposing.
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Figure 4: In-domain Fl performance on the MedTS cohort datasets. FORMED achieves SOTA level performance across all datasets in all metrics. Numerical results are shown in Table 4. Other metrics are included in Section E.
Furthermore, all datasets within the cohort and for subsequent adaptation are split following strict patient-independenr settings (Wang et al., 2024b; Wang et al.). This ensures that the test sets contain subjects entirely unseen during training, rigorously evaluating the model's robustness against intra-dataset heterogeneity and its capacity to generate new patients rather than memorizing subject-specific features.
To specifically test the adapting stage and FORMED's generalization to entirely new, unseen tasks and potentially different data characteristics, we also include out-of-domain datasets (ECG200 (Olszewski) and StandWalkJump (Behravan et al.)). Performance on these datasets, especially with limited data for adaptation, will demonstrate FORMED's utility under data insufficiency.
Baselines. We compare FORMED with 15 baselines, including 11 established TSM (Wang et al., 2024b) and 4 TSA models for direct comparison. The TSM models are trained independently on each dataset, including Autoformer (Wu et al., 2021), Crossformer (Zhang & Yan, 2022), FEDformer (Zhou et al., 2022b), Informer (Zhou et al., 2021), iTransformer (Liu et al., 2023), MTST (Zhang et al., 2024), Nonformer (Liu et al., 2022), PatchTST (Nie et al., 2022), Reformer (Kitaev et al., 2020), Transformer (Vaswani et al., 2017) and Medformer (Wang et al., 2024b). They represent the conventional approach, used to verify the benefits of FORMED's repurposing transferable domain knowledge.
repprpoosin stage in learning
The TSA models aim share knowledge across tasks. TimSsFM-7SA is key baseline, created by adapting the same TimesFM (Das et al., 2024) backbone with a task-specific CNN head for classification. This allows for a direct evaluation of the additional benefits provided by FORMED's novel classifier design and the two-stage repurposing/adapting strategy over a more straightforward adaptation of the same foundation model. The additional TSA models, PutchTST-TSA, Reformer- TSA, and iTransformer-TSA are variants of their TSM counterparts. Their backbones are shared across tasks with task-ipec heads, and they are configured to have similar parameter amount to FORMED for demonstrating the superiority of using pre-trained foundation models.
Evaluations. The effectiveness of our method is primarily demonstrated through its classification performance on the test sets, using metrics such as accuracy, precision, recall, F1 score, AUROC, and AUPRC. Strong performance on these aatient-independent test sets will underscore FORMED's s ability to overcome intra-dataset heterogeneity. The generalization ability to unseen tasks, particularly under conditions of potential data insufficiency and inter-dataset heterogeneity. evaluated through adapting experiments
the small, out-of-domain datasets. These experiments, crucial for assessing the efficacy of the adapting stage where only minimal parameters (CEs and LQs) are learned, are conducted on five random seeds for all models, with results averaged to ensure reliability.
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Accuracy
Precision
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Figure 5: Adapt-time scaling on unseen, out-of-domain dataset. FORMED's ' performance scales well with & following power law, and outperforms TimesFM- TSA starting from k 64 ECG200, and from k- = 16 on Stand Stlk/ualk Numerical results see Table 5.
5.1 EVALUATION ON REPURPOSING: GENERALIZE UNSEEN SUBJECTS
Setup. For repurposing datasets in MedTS cohort, trained 55 TSM models (11 models for each), and TSA models with 5 task-specific heads each. Our FORMED model is trained on all 5 datasets, using a fixed 16 for all datasets.
Results. The results compellingly demonstrate that the proposed repurposing, which allows the SDA capture shared MedTS domain knowledge while CEs and LQs handle task-specifics, is more effective than both TSM and TSA approaches for complex classification tasks. As shown in Figure 4, FORMED achieves sigmificant improvements over all baselines. This is particularly pronounced on medium-to-large datasets, such as ADFTD, PTB, and PTB-XI., where performance gains to 30-40%. On the relatively small TDBrain dataset, which represents a simpler task where TSMs' performance also saturate, FORMED performs par with the strongest TSMs while maintaining clear advantage over TSA methods. This superiority stems from FORMED's ability learn from the collective knowledge of the diverse MedTS cohort, demonstrating that SDA learns generalizable patterns relevant to MedTS classification. Such robust learning across varied datasets translates directly into addressing intra-dataset heterogeneity, i.e., better generalization to unseen subjects.
Interestingly, TSA models generally underperform compared to TSMs, and significantly to FORMED. This might suggest that their simpler task-specific heads shared backbone less effective at navigating the substantial inter-dataset heterogeneity within the MedIS cohort than dedicated SDA, CE and LQ architecture. To rigorously verify this, conducted an extended baseline comparison as shown in Section F, testing simple architectural variants such as replacing our classifier with an MLP or adding LoRA fine-tuning. These modifications still failed to bridge the performance gap (e.g.. TimesFM + MLP achieved only 48.8 18.84% accuracy on ADFTD vs. FORMED's 's 66. 83%). confirming that the foundation model's 's latent features are not linearly separable and require the non-linear decoding provided by our SDA module.
5.2 EVALUATION ON ADAPTING: GENERALIZE TO UNSEEN TASK
Setup. this evaluation, we assess FORMED's ability to generalize to entirely new, unseen tasks, critical test of its adapting stage and its robustness to inter-dataset heterogeneity and data insufficiency. We use the TimesFM-TSA model as a strong baseline. The FORMED model is obtained from the repurposing stage; its backbone and SDA parameters kept frozen. Only newly initialized CEs and LQs are trained for these new tasks, demonstrating data-efficient adaptation. We also explore how performance scales by varying the number of queries per class (k) from 4 to 2048.

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Results. The TimesFM-TSA baseline, lacking sophisticated pre- pre-adaptation the MedTS domain for its classification components, can struggle to generalize from limited data on new tasks, often overfitting the training data. In contrast, FORMED, by leveraging the rich MedTS domain knowledge captured in its frozen SDA during the repurposing stage, demonstrates superior adaptation. As seen in Figure 5, FORMED generally outperforms the TimesFM-TSA baseline these unseen datasets, even with less adaptable parameters (FORMED at k = 16 outperforms TimesFM-TSA with only 1/6 of the parameter). Notably, the baseline TimesFM-TSA exhibits high variance legg. +2.63% std in F1 on ECG200) due to overfitting. In contrast, FORMED stabilizes significantly as the number of queries & increases. The performance follows power law improvement;
1024, FORMED achieves 87. 87.65% accuracy with reduced variance (+2.33%) demonstrating that the MoE design provides robust density estimation even with limited training samples. This scalability and efficiency in the adapting stage validates FORMED's strength against data insufficiency and inter-dataset heterogeneity, making it particularly well-suited for real-world clinical applications where new diagnostic tasks may emerge with limited available data.
DISCUSSIONS AND CONCLUSION
In this paper, we introduced FORMED, novel framework that repurposes general time series foundation models for robust and adaptable Med'SS classification. FORMED's core architectural innovation lies in its attention- based classifier, featuring SDA, CEs, and LQs. This design uniquely equips models to handle variable input lengths, diverse channel configurations, and dynamic numbers of output classes, addressing limitations of conventional TSM and TSA approaches.
Our comprehensive experiments demonstrate FORMED's s strong generalization capabilities, achiev- ing state-of-the-art performance for unseen patients within datasets (intra-dataset) and effec- tively adapting to unseen tasks (inter-dataset). This highlights two significant findings: first, the feasibility and effectiveness of leveraging powerful pre-trained foundation models as backbones for complex MedTSS classification. Second, it validates the superiority of our FORMED repurposing framework. The framework excels at capturing transferable MedTS domain knowledge within its shared components during an initial repurposing stage, which then enables highly efficient and data-scarce adaptation to new tasks by learning only minimal, task-specific parameters.
Despite these promising results, we acknowledge limitations. Our validation primarily utilized TimesFM as the backbone. While this serves as a strong proof-of-concept for the FORMED frame- work's efficacy, its performance and interaction with other diverse time series foundation models warrant future investigation. For example, replacing TimesFM with other foundation models that can handle irregularly sampled data or categorical data, will grant FORMED broader applicability. Additionally, the composition and scale of the MedTS cohort employed during the repurposing stage may also influence the breadth of the captured domain knowledge and the overall quality of the learned representations. For instance, we observe that the performance of FORMED during both repurposing (Table 4) and adaptation (Table 5) shows higher variance than other models, which may be attributed to the limited number and scale of datasets in the MedTS cohort and the impact of different splitting by chance. As evidenced in Figure 6, larger datasets like PTB and PTB-XL generally exhibit more stable training trajectories, while smaller datasets like ADFTD and APAVA show more variability across seeds and splits. Future work can explore expanding the pre-training cohort and incorporating advanced joint training strategies to stabilize the FORMED framework.
In conclusion, FORMED presents a significant step towards more generalizable, adaptable, and data-efficient adaptation framework for time series foundation models, as well as practical deep learning solutions for the unique challenges posed by medical time series analysis.
ACKNOWLEDGEMENTS
This work is partially supported by the U.S. National Science Foundation under Grant No. 2245894. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funders.
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A COMPARISON OF ADAPTATION TECHNIQUES IN FOUNDATION MODELS
As discussed in Section 2, adaptation techniques for foundation models mainly includes Prompting. Fine-tuning, Re-programming and Re-purposing. We have introduced re re-programming and re- purposing, and here we provide a brief overview of the rest, prompting and fine-tuning, and compare these techniques based on three aspects: Data Efficiency, New Task Type, and Generalizability.
Prompting & tineetning: Both are common adaptation techniques for foundation models, where prompting involves conditioning the model with specific instructions cues, either handcrafted (Zhou et al., 2023b; Reynolds & McDonell, 2021) or learned through data (Zhou et al., 2022a; Liu et al.), and tineituning involves updating the model's internal parameters dedicated dataset (Howard & Ruder, 2018; Ding et al., 2023). While they focus on different aspects of adaptation, they share the commonality of not altering the model's Is core architecture, therefore the functionality of the model remains unchanged, e.g., model for forecasting remains forecasting model, and dedicated task head still required if the task changes (Liu et al.). Moreover, fine-tuning is often more data-intensive, as it necessitates updating the entire model's parameters, whereas prompting typically only requires learning limited set of task-specific embeddings or prompts.
In general, these techniques can be categorized based on three aspects: Data efficiency, as the scale of dataset used for adaptation, typically closely related to and thus constrained by the number of parameters updated; New Task Type, as the ability to adapt to new tasks that are different from the original task, such as from forecasting to classification; and Generalizabiliit, as the ability for the adapted model be used on unseen datasets and share knowledge across tasks. Table provides comparison of these techniques based on these aspects.
B IMPLEMENTATION DETAILS
We take TimesFM (Das al., 2024) as the backbone for repurposing based on our preliminary comparative analysis of existing time series foundation models. TimesFM is pre-trained on a largest- scale dataset of diverse time series data for forecasting tasks and is able to capture general time series patterns within dynamic length of historical input. To repurpose it for MedTS classification, we can break down the model's anatomy into three parts, the input patching network, the stacked Transformer, and the output prediction network.
Input Patching Network. Given univariate time series input æ E R and binary mask m {0, 1) with length T. they are first broken up into patches X E R and M E 1)xPP in a non- overlapping fashion, where P is the patch size and L f is the number of tokens. Each patch X,, is the concatenation of P consecutive elements of the input sequence x a non-overlapping fashion and so is the M.... The X,;; and M,, denote the i-th row of X and M, respectively. The
sequence of patches X and M are then projected to sequence of tokens Z E I LL in the model dimension D using an input residual block:
Z = IIputtptttttt Res wesnnntResiduaall.. wew.eiiiillloo......
M..)
(8)
Stacked Transformer. Before passing into the stacked Transformer, the positional encoding will be added to the tokens to form the input sequence 2 E RLIDD The stacked Transformer is then applied to the input sequence Z to capture the temporal dependencies and extract features using casual self-attention, outputting feature rich tokens H E RRLD.
Zi = Zi. @ PossitionallEnoodnnn())
H =tt -ttackkedrransfooreeeeeee
22..... Zr,5 mi, rit2, m,)
where ri; = min (M..)) is the mask for the i-th patch for masking out completely empty ones.
Output Prediction Network. The output prediction network is residual block layer that maps the last output HL, from the Transformer back to the original input spaces á E RR,, forming the prediction of the next N time steps:
t = OutputResi BBoock (H..))
(10)
In summary, the duty of prediction lies solely on the last output prediction network, while the input patching network plus the stacked Transformer be viewed as feature extractor that maps the
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input time series sequence of feature tokens H (Figure 3). This be easily extended to process multivariate MedTS by processing each channel of input individually and stack the extracted features H Rxxxx for data of C' channels. This will serve as the backbone feature extractor for the downstream classification model.
C EXPERIMENT SETUP
C.I EXPERIMENTAL SETUP
The experiments carried out several hosts. The following list summarizes the hardware and software configurations used in our experiments:
Table 2: Environment setup.
Host No.
CPU
Memory (GB)
GPU
Intel Core 19-1990-90 19-10900X
32
NVIDIA RTX A5000
AMD Ryzen Threadripper PRO 3995WX
512
NVIDIA RTX A5000 4
AMD EPYC 7713
1024
NVIDIA RTX A5000
AMD EPYC 7513
256
NVIDIA RTX A6000
Software/Package
Version
Python3
3.13.3
PyTorch
2.7.0
CUDA
12.4 12.6
C.2
CODE AVAILABILITY
The code is publicly available at httpp:://itthutee httt:p////ggtttteccc////////////
C.3 DATASET AVAILABILITY & PREPROCESSING
The preprocessed datasets in the MedTS cohort are obtained from the authors of Wang et al. (2024b). The datasets used for adapting are loaded through sktime API and no additional preprocessing performed.
C.4 DATA SPLITTING
For datasets in the MedTS cohort, the datasets are split into training, validation, and test sets in the ratio of 6:2:2 at patient level according Wang et al. (2024b); Wang al.. The adapting datasets not clearly defined in terms of patient, so we perform the best effort to split the datasets into training, validation, and test sets in the ratio of 8:1:1 at recording level. The splitting seeded and stratified to ensure that the training, validation, and test sets have similar distributions of the target classes. The training set is used for training the model, the validation used for early stopping, and the test set is used for evaluating the model performance.
C.5 MODEL TRAINING
The frozen pre-trained backbone TimesFM model the version from the official GitHub repository, with 50 layers and model dimension of 1280, and patch size of 32. The classifier in FORMED is initialized with default initialization method, and the embeddings are initialized to normal distribution with 0 and ² 0.1. The model is trained with AdamN optimizer with weight decay of 1 x 10 and a custom log-normal normal learning rate schedule. For each epoch t, the learning rate calculated as:
t= 0
CN(1:1,,2,7)
exp ( (-0nt--- (Int-InT- 2a2
otherwise
(11)
LN(t; 22.
Ir(t; 02 22,") = =1xmin +
(1Emax 1rmin)
max, CN(t
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where μ = =1, 10, 1Imin = 1 10 and 1rmax x 10 in our experiments. This creates quick warm-up phase followed by a gradual decay of the learning rate for annealing. The Figure 6 shows the typical training trajectories of FORMED during repurposing.
During each epoch training, the model is trained on 100 batches of data from each dataset sampled at random order. The batch size is tailored for each dataset according the available samples in each dataset, making the 100 batches data approximately equal to one effective iteration of the whole training set. Additionally, gradient clipping of 1.0 is applied to prevent exploding gradients and overshooting in earlier epochs. The model is trained for 100 epochs with early stopping on the validation based on the average Fi-score with patience of 10 epochs. The same training procedure is applied to all TSA and our method.
11 Vajectary (2odd44)
PL Tagecoar (22dd-42)
160
62-
20-
T00kmh
63
100
20 46
83
300
Figure 6: Typical training trajectories of FORMED during repurposing from two different seeds. FORMED can keep improving long training period, despite that the gain might not be consistent on different partition of datasets (e.g., validation and test on ADFTD in the right figure), and can vary depending on the exact random seed (e.g. test on APAVA in two figures).
C.6 HYPERPARAMETER TUNING
The initial hyperparameter & for the classifier in FORMED was not tuned due to the high cost associated with repurposing extra-large models like TimesFM, but rather set to an arbitrary value of k = 16 for all datasets. However, we do perform grid search for the hyperparameter during the adapting process, as shown Figure 5. We initiate the search with powers of 4, until the performance shows no significant improvement. Then perform finer search with powers of 2. We find that 256 ~ 1024 is good range for adapting to small datasets. This search is very efficient as the computation cost of increasing & is often negligible compared to the computation cost of large model backbones, despite being linear in & theoretically.
D DATASETS
Here we provide the details the datasets Table 3 used as the MedTS cohort for repurposing in Section 5. The datasets publicly available, and we follow the pre-processing and splitting procedures as in Wang et al. (2024b).
Table 3: MedTS Cohort Datasets.
Dataet
Type
Subjectt
# Sample
Sumpling Rate
Sampling Length
# Channel
A Classes
(Miliidous al. 2023; Miltiadous et
APAVA (Escudero di al., 2006)
23
5867
TDBrain Evan Dijk et al, 2022)
BBG
72
62400
256 Hi
256
33
PTB (Goldberger ol., 2000)
BCG
196
64.356
250 Hz
300
15
PTB-XL. (Wagner al., 2020)
BCG
17596
250
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Published as a conference paper at ICLR 2026
E EXPERIMENTAL RESULTS
Due to space limitations, here provide the rest of experimental results of FORMED on the MedTS cohort datasets in terms accuracy, precision, recall, AUROC, and AUPRC in Figures to 11, along with the full comparison table in Table 4 and adapting results in Table 5,
80
Accuracy (%)
70
85
85
75-
80
180
85
70-
60
75
50
40
PatchiSST, Autoformer, FE FEBformr, MIST. Mitasformee, Cressformer, Nonforrmer Informer, Transformer, Reformer, Medformer RSformesTAA, ITaansfomme-Ss. PMENNTST-SA. TimesFMATSA
RM
Figure 7: In-domain accuracy performance the MedTS cohort datasets.
ADFTD
APAVA
TDBrain
PTB
PTB-XL
40
100
100
100
60
80-
96
95
80
70
Predision (%)
70-
60
85
50
75
75
80-
70
70
PatchiSST. Autoformer, FEDformer MIST, Maansformr, Cresaformer, Nanformer, Informer, Tranfformee, Reformer, Mediormer Retorme-TShh, ITansttmmeeTSS. PatcNTSTFTSA TimesFiMSSA
FO FORMIED
Figure 8: In-domain precision performance on the Med'TS cohort datasets.
F EXTENDED BASELINE COMPARISON
To rigorously evaluate the effectiveness of FORMED, conducted extensive comparison against both traditional machine learning approaches and various configurations of the TimesFM foundation model foundation. The results are summarized Table 6.
F.1 BASELINES SETUP
We compared FORMED against MiniRocket (Dempster et al.), a strong machine learning baseline for time series classification. Additionally, evaluated four distinet variations of the TimesFM architecture to isolate the contributions of our training strategy and classifier design:
TimesFM + CNN: The baseline used in main experiment, i.e., the TimesFM- -SA model, utilizing CNN head engineered for balanced performance.
18

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Published as a conference paper at ICLR 2026
ADFTD
TDBrain
PTB-XL
90
100
1.00
100
90
90
Recall (%)
90
60
80-
60-
75 75
50
50-
70-
PatchT'ST, Autotomer. FEDfomer, MTST, manstorer. Crosotormer, Nontorrer, Informer, Transformer, Reformer, Medformer
Reforma-TSa ISA, ITTandooomrTS....... PatchT51/15A TimesFMTSA
PO D
ADF ADF Aurre gure
9: rn-dol 100
performancsson the MedTS oohort datasets. 100
PTB-XL
100
100
100
90
Auroc (9)
75-
90
80
70-
70-
50
Adrigure 10: In-RO perfomoance on the MedTSrgohor datasets. PTB-XL
95
80
95
90
70
Aupre (%)
70-
40
30
70
Medformar
Figure 11: In-domain AUPRC performance on the MedTS cohort datasets.
TimesFM + MIP: naive implementation utilizing linear classifier head (identical to all TSM baselines), serving as direct probe of the frozen backbone's 's features.
TimesFM + MLP + LoRA: Extends the MLP baseline by fine-tuning the TimesFM back- bone using Low-Rank Adaptation (LoRA, Hu et al.).
19

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Published as a conference paper at ICLR 2026
TimesFM Attn: Shares the identical model architecture (Attention- based classifier) as FORMED but is trained individually on each dataset (single-task learning) rather than jointly.
F.2 PERFORMANCE ANALYSIS
FORMED demonstrates superior performance across the majority of datasets. On the challeng- ing ADFTD dataset, traditional methods like MiniRocket struggle (49.02% accuracy). While the TimesFM CNN baseline improves this to 54.77%, FORMED significantly outperforms all baselines with an accuracy of 66.83% and an F1 of 63.66%.
Notably, the comparison between TimesFM + Attn and FORMED highlights the efficacy of our joint training paradigm. On PTB, the single-task TimesFM + Attn achieves 93.48% accuracy, whereas the jointly trained FORMED improves this to 95.74% Similarly, APAVA, FORMED achieves 85.10% accuracy compared to 65.90% for the single-task equivalent. This validates that the model benefits from the shared representations learned across diverse medical time series tasks.
While simple classifiers like TimesFM + MLP perform competitively on specific, usually smaller datasets like TDBrain (93.60% accuracy), they lack the consistency of FORMED, which maintains high performance across complex multi-class scenarios like PTB-XL, achieving 77.72% accuracy the MLP's 65.22%.
For the LoRA- LoRm-auumented TimesFM + MLP, while shows improvements over the vanilla MLP on some datasets (e.g., PTB accuracy from 87.12% to 92.76%), it still falls short of FORMED's performance, indicating that merely fine-tuning the backbone is insufficient without the tailored classifier and highly inefficient. Also note that LoRA is orthogonal classification head design, and can be potentially combined with FORMED for further improvements.
G ABLATION STUDY
To justify the architectural components of FORMED, we performed a comprehensive ablation study isolating the effects of Positional Embeddings (PE), Channel Embeddings (CE), and the Mixture of Experts (MoE) strategy (k=16 vs. k=1). The results are detailed in Table 7.
The full implementation FORMED (incorporating PE, CE, and MoB) consistently yields the most robust results. For example, on the ADFTD dataset, removing any single component leads to significant drop in Fl: without PE (46.26%) without CE (42.54%) and without MoF (46. 76%), compared to the full model's 63.66%. Similarly, APAVA, PTB and PTB-XI,, the full model outperforms all ablated versions, underscoring the synergistic effect of these components.
While certain ablated versions perform better on specific datasets, such as the configuration without Channel Embeddings achieving marginally higher accuracy on TDBrain (88.08%) compared to full model (87.47%) ) the full FORMED model offers the best generalization. It avoids the catastrophic performance drops seen ablated versions on harder tasks (e.g., the drop to 64.22% on APAVA when CE is removed). This confirms that both PE and CE essential for handling the structural variety of MedTS, while MoE effectively scales the model's capacity for complex classification boundaries.
20

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Published as a conference paper at ICLR 2026
Table 4: Results on MedTS Cohort for disease classification. Best results from non-TSM models are bolded and best results of all models are underlined. TimesFM-TSA shows great improvement over other TSA models and achieves competitive performance with the SOTA TSM models. While our model, FORMED, consistently outperforms the all other model on all datasets.
Datasets
Adapiation
Models
Accuracy
Precision
Recall
Fl score
AUROC
AUPRC
Antoformer
45.25
13.67
42.96:2e
42.59
6102sus
43.10:120
Crostormer
50.45,331
45.57.144
45
45
66.45
48.33,220
FEDformer
46.30a5:4
46.05
44:22:122
43.91.137
62.62a125
Informer
48.45:115
46.54
46.06:124
45.74
65.87
47.00s120
l'ransformer
52.60.154
46.79g137
47.28
1
67.2651166
49 13
TSM
MIST
45.001200
44.70a131
45.05
44.31
62.50.41
45. 16
Nonformer
49.95s118
47.7
47.46:130
46.961139
66.23
ADFTD
PatchTST
44.37.005
42
42.0611/
41 17
60.08s1
17.49:1779
(3-Closses)
Reformer
50.78:117
49.64.1-
49 19.89
47.9418
(9.17
11.73:104
Transfommr
50.47a214
49.13
48.01
48.09
67,93s1.9
48.93-123
Medformer
53.27g154
$1.02g1 57
50.71gm
50.65:1 51
70.93g119
$1.21 13
Traanformer- ISA
52.51am00
45.31
45.32s54T
39,56s433
60.78asas
43 13.36444
TSA
PatchTST-TSA
TSA
42.53
53.364227
24.24
16.97ssM
43:93s2.
25. 31
40,07a330
52.84
60, 60,91
4474
TImesIM-ITSA
54.77am3s
50.76ax1s
51.12
30.58
68.81
12.42.1033
GA
FORMED (Ours)
66.83-11
63.54-m.
65.25
6366-111
17.70
66.00:1834
Antodormer
68.64113
68.48
68.77
08 04
75.94:341
38, 1438
Crostormer
73.77a159
79.29
68. 1m
68. as
12.39
72.05 8346
FEDiormer
74,94a21
74.59
73.56
73:5
83.72st.7
Informer
75.17
69 te
70,46:441
Transformer
74.55a144
14.77a210
71.76
72.30.1.79
85.59
TSM
MIST
71.142199
79.30=2m:
65.27=2..
61.01.3.1
68.87:2M
71 11:06:140
Nonformer
71.89233
71800=445
69.4
69. 74:244
70.55=2.
70 78
APAYA
P
67.03
28.76a122
59.91
55.977331
65.65
67.99
(2 Classes)
Reformer
78.70:21e
82.50=335
75.00:1a1
75.92s132
73,942141
16.04.114
Transormer
76.30
77.6/ess
73:09:3m1
23.75
72.50
Medformer
78.74a044
81.11a334
75.40.00
76.31am
83,20
83.66100
Transforme--TAA
63.0621529
12.70 a0M
62. 1
=3.71
74.00 2
TSA
Reformer- TSA
PstchTST-TSA
66. 35
71 62:130
75.70
68 18.661555
65 88888 16
68. 05:440
64.71
12.79-141
73.17
75.61 25.61:2 26:3
71 00266
TimesPM-TSA
75.00sm1e:
75.83
75.63
74.77
83.02
86 0lanne
GA
FORMED (Ours)
85.10.531
87.34-244
83.11
82.45:124
91,22
92.51:740
Autoformer
87.33
88.06
87.33
26:344
93.81
Cousformer
31.562219
81.97=25>
81 11.56
91.200a1.
91.
FEDiormer
78. 13
78.52a1 91
78 13g
78
04
86, 16.56::1
86 48
Informer
$9.02:120
89.43s214
89.02s2.0
88. S5.98:154
96.642as
96. 75
Transormer
74.67:11e
74.71
74.67au
74.65
83.37
TSM
MTST
85
Nonformer
87.88:244
98.8611..4
87.
$7.78:124
97.05=135
TDBrain
PatchTST
79.25-2339
79.60=447
79 25
79 70.20
87.95
85 36
(2 (-Clusses)
Reformer
87.92=2m
88.6421.43
87 a229
87 15185
96, 96.30
96 40.0s
Trunsformer
87.17a107
87.99
87. 87.17
87. 10
96.28
96 34
Mediormer
89.62:503
89.68
59.62
99 89.62
96.41
96 26000
Vrransformes-SAA
66.11219
63.57
62. a4
62. 53
69.44.11
TSA
Reformer- TSA
PatchTST-TSA
13.03
18:430
85.27
83.74
80.8
73
74 38
11000:44
78.75
90.39
26
15
TIMIsPM-TSA
85.00 9
81. 81.71g3
82 29
92.22
87.96 18:34
GA
FORMED (Ours)
87.47
87,58
86.95:11.2
$6 16338
94,20
$7.02 1092
Anloformer
73 15.3522
72.11
63.24
63 69
78.54
74 25
Crossformerr
88. 58.55
FEDlormer
76.05-224
37.58
10:3as
67. 14237
85,93
Informer
a2.87
69 19:20
70.84
22.09
20.02
Wransforrmrr
83 83.89:2m11
88.25 18
76. mm
70 100661 cs
91.18a116
90 27
TSM
MIST
76.59:1150
79.88:1.0
66.31
67. 67.38
86.86i225
$3. 75
Nonformer
78, 23.66an.
82.77
69. 12
70.90:11e
89.37
86 67
PTB
PatchTST
74.7421k
76.94.151
63. 29
64
78
(2-Closess)
Reformer
11
81.72 sl
68 20,3 s4
Transormer
77.37+11
81.8
67.
65. 17:2.11
90.08=1.2
Medformer
83.50a2m
85.19
77.11
79 18:131
22.81
90 32
Transformet-SAA
83. 118
80.08
78.11776
78 84:44
93.18
98 inzs
TSA
PatchTIST:TA
Reformes-TAA
74 14447
3621
71. 15
78677499
79.37a5
68 70
79 95
91.95gm
93.47
98
98 64
TImesF
90.17
86.843344
3613
94,22
GA
FORMED (Ours)
95. 75.74:248
94.35 422
91,03:5
92-53-45
97.05-2m
99.05-471
Autoformer
68
$1.60 61
49. 10
48 33
82.04g1 41
11.93:771
Crostormer
73 10.30
65.06
61.25
62 14
90.02.03:
07.43 1022
FEDformer
57. 20
49.04
47.8 37.89
82.13
52.3 31
Informer
71.43.601
59. 12
60:44:8
Tranndormer
00.28:0022
49,59
56.20.0.11
8617
TSM
MIST
72. 1.144
63.84
60.01
61 33
98.9221
Nonformer
70.56.204
61.57
57. 37.75
50 10
88.12gn 9
63.40 40
PTB-XI.
PatchTST
73.23
65.70ga44
60 82
62.61:344
99.74.119
67 1222
(5 (S-Clnsse)
Reformer
71.724041
63.12auuz
59 20
60.69
88 80
64. 72ana
Transformer
70.59:2044
61.57
57 62622a
50 15.051115
28
36200
Medformer
72887403
64.14
60
62.02.333
89.661015
Transforme--SAA
63.842004
53.14
45.38
46.07.114
:2.65
52.01a1e
TSA
Reformer- TSA
57.61.333
57.25
$1.42115
49.92a345
38.06
46.09.1
36.88.129
43.93:338
81.71a177
79.24.um7
50.1 12r
45.
TimesFMMTSAA
71.08
62.73
58.28
59.97a84
63.45.00
GA
FORMED (Ours)
77.724728
71.75
69.06:1137
70.12:194:
91.72
73.95.1811
21

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Published as a conference paper at ICLR 2026
Table 5: Results on adapting unseen datasets. Best results are highlighted in bold.
Datasets
Model
& factor
Accuracy
Precision
Recall
Fl score
AUROC
AUPRC
T-mSSE
N/A
66,32s471
40.89
52.95
45.23
80,2629.79
91.24s5.22
18.63
55 17
51.28
65.90
81.64g
43.16:1552
13.68:
45.00
39.40=1406
45.13:1554
71.92
16
57.89.1.77
49.65
11.28
49.86:21441
68.46
85.77
32
18
19
54 16
63.33
80.85
ECG200
64
70,53+471
65.73
62.31
62, 62.24
73.85=7223
88.05
FORMED
128
70.53+4.71
65.3 65.39
60.51
60.59a6.7
75.90=4.19
89.96=1.8 181
256
84.21
82.401857
82. 18:444
80.56
19.23
95.35
512
87.37
85. 38.38
88, 08:
85.87
95.90.446
98. 1892
1024
88,42=
86.67
91,54
87.68-2.33
98.21=115
99,23
2048
87,37=471
85.41+487
88.97
86.25
95,90=3.56
98.22
M-TTA
N/A
33.33
13.83
33.3
32,41
72.00
44.89
21 7..33
19.23
21.33
19.69=7.22
44.00=10.6
42.07=7.44
8
18.67=12882
19.18
18.67
18.68=12.19
46.
46.13:12..7
40,63
1011
12.
36 11:00111111
34. 55
13.60=10.11
1x
32
16.00
37.45
16.00
35.87=1737
17.33
15.11
Stand JalkJJump
FORMED
64
15.00
36.30+
40.00::5563
37.50:11377
56.80
50.30 13.12
128
16.67
43.96111.29
46.67
44.26:117.8
63.60.11.7
55.75
256
46.67=1333
47.33 17.33+11457
46.67
46 46.29=14255
63.60 13:60
53.81=204
512
64.00=1116
30.25
64.00:11.16
64.52-11048
68.13-5.0
57:27=425
1024
54.67=7.30
55,24
$4.67=77.0
54,23=7244
62.13:1
53,30
2048
60.001887
62.29
60.001664
60.12:167
65.33
56.341220
Table 6: Additional baseline comparison on all datasets. Best results are bolded.
Dutasets
Model
Accuracy
Precision
Recall
F1 score
AUROC
AUPRC
MiniRocket
49.024.00
41.37=001
43.64
41.752016
PatchTST FORMED
45.05
45.43=391
42.04
36.68
59.22:3.0
41.10
ADFTD (3-Classes)
TimesFM + CNN
54.77
50.76=4.15
$1.128784
50.58
68.81
12.42
TimesFM + MLP
48.8412.0
41, 41.94
43.47
41.72a123
60.73
43.062141
TimesFM ML.P LoRA
49.23
45.81=120
46.04::2.9
45.41
63.8341134
TimesFM Attn
52.77
46.89=1.20
46.34s:053
45,25
66.174099
48.02#081
FORMED
66,83:1835
63.54=21.37
65.25
63.66:21.67
77.70=1616
66.00:30.5M
MiniRocket
67.0814.6
71.38-351
70.17
66 66.90
PatchTST FORMED
69.91
69.11 10:0
68.5421181
68. 14.1
79.32:11
86.627 7636
APAVA (2 Claasssss)
TimesFM + CNN
15.00
-9.83
75.63
14.77=1007
83.02+12263
86.03
TimesFM MLP
83.75
83.71=331
83.02s2.68
83.16:304
89.63+a192
87.95:4405
TimesFM MLP LoRA LoRA
81.1414.6
82.04_2.M
80.94
80.481422
90.20
90.00a2.11
TimesFM + Attm
65.90 94
67.33=1190
66.41:1195
64.97=1.5
72.77:2347
68.97:2604
FORMED
85.10
87.34-7748
83.11
82.45
91.22:4771
92.51=7700
MiniRocket
86.40 66.40
90.06=0.51
83.26
84.81
PStchTST FORMED
89.00
89. 19.68
89.00
88.95.1113
96.76a1.27
96.92.1115
TDBrain
TimesEM + CNN
83
85.00=7.89
81.71
82.49
92.22:11.0
87.96
(2-Clusses)
TimesFM + MLP
93.60
93.64-0.3
93.11:
93.
96. 14777
92.80=3330
TimesFM MLP + LoRA
90.57+1.l
90.48-2207
90.05
90.
16:
03.86622.30
87.79
TimesFM + Attn
86.87413
90.05
83.98
85.42.113
96.431142
95.34
FORMED
87.47
87.58=
86.95=10.20
86.38.934
94.20
87.62
MiniRocket
93.46:051
91.61=1.17
14.
87
PatchTST FORMED
90,29
88.19
71.61
76.03
94.79
98.98:2027
PTB (2-Classes)
TimesFM + CNN
94
90.17 90.17=2229
86.8443.1
88.362225
94.22:43.9
98.29
TimesFM + MLP
47
77.20=003
76.70a1.1
76.922007
1..80
91.45
TimesFM + MLP + LoRA
92.76+204
47.80=440
85.80=3.00
36.66:3:221
35.83
94
TimesEM + Attn
93.48
90.60=
85,36
87.6420.0
88.00
38.00
97.17
FORMED
95.74:240
94.35=426
91.03
92.53:4498
97.05
99.05:a071
MiniRocket
73
68. 29.
58.6 58.64-20m 16
60.7620115
PatchTST FORMED
62.14
55.51=1153
45.21
46.04=3.30
83.06
52.97:2099
PTB-XI.
Times/M + CNN
71,0810
62.732200
58.28
59.97.041
38.49.1037
23.48.2099
(5-Classes)
TimesEM + MLP
65.22.1.12
55.26=1332
52.63
53.461008
81.35
$1.24:11.
TimesFM MLP+ MoRA LoRA
67.102064
58.04g111
$4.81
55.292079
85.04-10.9
$7.07:20.2
TimesEM Attm
72.51+0a8
64.19=030
59.67
61.13
89.31
65.72440.3
FORMED
77.7247.10
71.75
69.06:11337
10.12::1082
91.72:1342
13.95:11.11
22

---

Published as a conference paper at ICLR 2026
Table 7: Full ablation study results of FORMED on all datasets. Best results are bolded.
Datasets
PE
CE
MoE
Accuracy
Precision
Recall
Fl score
AUROC
AUPRC
48.81
42.92
43.67
42.54+1.
61.43+1.0
43.83+1.24
X
/
53.77
47.80
47.55
46.2610.76
65.4610.50
48.20110.99
ADFTD
X
45.87::224
40.8311.54
42,65
40.57
60.4312248
42.17:2.44
(3-Classes)
54.08=215
48.61+126
16+1.3
46.78+1.5
66.43+2200
49.52+296
66.83
63,54
65,25
63,6612147
77.70
66.00
64.22.1600
64,231488
64.27
63 63.6415.58
69.9615.47
67.50.17.8
67.49
66.84
65.95
66.07+6612
70.92+495
71.62
APAVA (2-Classes)
69.08:4.39
68.7444661
67.93
67.89
73.25
73.52+249
65.54=4425
65.39+4.38
64.84+44.s
64.50
69.52+33.2
70.18+6697
85.10
87.34.7.66
83.11
82.45.124
91.72
92.51.77.0
38.08
88.83
86.57:4414
87.23
96.
92.8014431
x
87.66=5.12
88.68+4.68
85.87+6.26
86.63+3775
96.05+2..8
92.3945.67
TDBrain
X
16.1312.4
87.731200
83.81
84.87.129
96.5810.77
94.49
(2-Classes)
87.57
87.8113322
86.42:1304
86.87.5.00
95.85
93.0416.98
87.47
87.5818.14
86.95
86.3819.34
94,201...5
87. 87.62.1082
93.70=n:65
90.01
87.17=1.27
$8.42200.8
95.65+0..1
98.88
89.86=1.u5
82.24=2265
82.05
82, 1..03-+1.044
91.48
97.71+0044
PTB
93.2310078
88 18.65
87, 87.02.12.66
87.7011.61
95.7610.82
98.93.10.9
(2-Classes)
90.92+076
84.41+1.46
81.944+2.044
83.00+0.00
91.47++3.66
97.63+1130
95.74:22.0
94.35
91.03
92.53
97.05
99.05:a0.1
70.03
61.82+1.00
55.51 +3,32
56.55+3992
87.22+1.72
61.63++3.12
66.32.129
57.681491
51,9613.84
52.60
85.38:1294
$7.92.14.99
PTB-XL
(5-Classes)
71.53
63.31
17.63
19:1111
18.61
1:0.6
64.38:+1.35
68.66+1124
59.89+1.29
55.32+1.45
16.1641.63
87.28++0.74
61.00+1.44
77.72.7.10
71.75
69.06
70.12
91.72.33.4
73.95
23


Reviews:
[{"review_id": "CorCAhJfMO", "reviewer": "Reviewer_fXsE", "summary": "The paper proposes an approach to repurpose the backbone of a time series foundation model for multi-variate medical time series. Authors freeze the backbone and adapt to the target task by learning task-specific channel and label embeddings. Results on real-world datasets show that this approach is able to both transfer information from pre-trained models and generalize to new tasks.", "questions": "Do you have additional ablation results on hyper-parameter and learning stability in the few-shot setting? Is there anything in the architecture of FORMED that is specific to medical time series?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "The paper is very well written and easy to follow. The proposed approach is relatively straightforward but principled and well justified. Experiments on real-world datasets show strong generalization with significant gains over leading baselines in both full and few-shot settings.", "weaknesses": "I would have liked to see more ablation studies. Hyper-parameter stability results for channel and label embeddings, stability in the few shot regime (it is hard to learn robust embedding from only a few examples), applicability to other tabular foundation models etc. I also didn't see anything specific to medical time series in the architecture of the proposed approach and generalization to other multi-variate settings could be interesting to test.", "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": "PgkJVY7F0S", "reviewer": "Reviewer_VzRy", "summary": "The authors propose a two-stage parameter-efficient fine-tuning approach for adapting time series forecasting foundation models to medical time series classification problems.", "questions": "- Why use a zero-shot univariate forecasting model for a trained multivariate classification task? This seems like an awkward decision for a backbone model, and since it's frozen, this mismatch could limit performance.\n- Some of the performance differences between the proposed model and baselines seem dubiously large given the differences in modelling decisions. For instance, for ADFTD, it's difficult to see access to the other four pretraining datasets giving FORMED such a massive boost over the other methods, including TimesFM-TSA, without data leakage or a mismatch in experimental procedures. Since code is not available at this time, reproducibility is limited. How would you explain these differences, and are you able to share anonymized code to reproduce them? Ablations could also help clarify this.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The paper tackles a relevant concern of adapting public models trained on general data to different domains, particularly addressing the needs of health data.\n- The new mechanisms used for fine tuning are promising and interesting ways to extend models to new tasks.", "weaknesses": "- The baselines being compared to are mostly models intended for time series forecasting, not classification. Even within the forecasting domain, SOTA models are not represented, and some of the included baselines are even known to be outperformed by linear models [1]. They are also mostly univariate and therefore not suited for multivariate classification tasks. Medformer is the only current classification model provided. It would also help to have comparisons to strong established baselines like MiniROCKET. (The baselines are also all described as \"SOTA\", which is not accurate.)\n- Existing parameter-efficient fine tuning methods like LoRA (or even end-to-end fine-tuning) could be used for either the Repurposing or Adapting phase rather than the new method the authors propose, but these are not discussed or evaluated against.\n- Ablations showing the contributions of different modelling decisions are not provided.\n- I have doubts about the design motivation and experimental soundness but would like to discuss further - see Questions.\n\n**Other content issues**\n\n- The \"Adaptation of Foundation Models\" section of Related Work only has one citation, which seems incomplete.\n- The second-last paragraph of the introduction seems to describe the data used in [2] as if it was a contribution of this paper - I think this should be clarified.\n\n[1] Zeng et al. \"Are transformers effective for time series forecasting?\" AAAI 2023.  \n[2] Wang et al. \"Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification\" NeurIPS 2024.", "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": "imivatB59X", "reviewer": "Reviewer_hoQL", "summary": "The authors introduce FORMED, for utilizing foundational time series models for adaptation to medical time series classification. The authors show improvement in results for classifying EEG and ECG signals.", "questions": "- The experiments focus on high frequency time-series such as EEG, ECG. How well do the authors think their improvements will hold for lower frequency medical time series such as HRV, GSR, etc?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The authors target a relevant problem in the medical field on the lack of work for addressing domain nuances around inter- and intra dataset heterogeneity.\n- The paper seems sound technically.", "weaknesses": "Avenues for improvement:\n- The authors can expand their experiments and ablation study to test more settings such as few-shot learning and include tabular results for easier inference.\n- Minor formatting required for page 18.", "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": "uasI14dvez", "reviewer": "Reviewer_xKQU", "summary": "This paper proposes a novel approach called FORMED for repurposing an existing time series foundation model for multivariate time series classification. Importantly, FORMED is able to accommodate arbitrarily numbers of channels and target classes, and separately captures medical domain knowledge and task-specific knowledge in the model architecture.", "questions": "Please see the concerns I raised in \"weaknesses\".", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The proposed repurposing strategy is quite clean and elegant\n- The experimental results look promising\n- I found this paper quite clear and easy to follow", "weaknesses": "My main concern at the moment is that some of the background information provided is inaccurate or is missing references that are relevant. For example:\n- Line 116: \"To date, no foundation model has been specifically designed for time series classification tasks, let alone MedTS classification[...]\" -> I would suggest toning down this sentence especially as I think some authors of various time series foundation models would arguably claim that their model was designed to handle forecasting **and** classification (along with possibly other tasks). For example, classification is considered in the original papers for MOMENT (Goswani et al, ICML 2024), TimesNet (Wu et al, ICLR 2023), Mantis (Feofanov et al, ICML FMSD Workshop 2025), and UniTS (this is already cited in the paper).\n- Much of the paper is on basically figuring out a way to combine/mix information across channels for medical time series. However, there actually already is a paper that does precisely this (\"Generalized Prompt Tuning: Adapting Frozen Univariate Time Series Foundation Models for Multivariate Healthcare Time Series\" by Liu et al, ML4H 2024). From what I can tell, FORMED is more sophisticated than generalized prompt tuning (such as having an additional adapting phase) but even so, it would be helpful to discuss what is different/novel (how information across channels is combined seems similar).\n- Line 181: \"NOT a simple modification of the output layer\" -> I think this is a strong claim that really needs to come with strong empirical justification since I do think that existing authors have been able to get successful classification results by essentially just changing the prediction head and fine-tuning or training from scratch (this is basically what various time series foundation models that are already applied to classification are doing).\n\nRegarding experiments:\n- While the paper focuses on using TimesFM as the base foundation model, I do think that it would be valuable seeing how FORMED works when using different base time series foundation models.\n- I find that the datasets considered are arguably too similar in structure (they're all ECG or EEG waveform data if I understand correctly). I would be helpful seeing how the results play out with other kinds of medical time series data such as EHR data (e.g., there are ways to preprocess MIMIC for instance to get it to be regularly sampled and to consider classification tasks).\n- A selling point early on in the paper is the idea of separately capturing medical domain knowledge and task-specific knowledge (e.g., line 94). I would imagine that these are often actually quite correlated so that they perhaps should not be that decoupled? Do you have thoughts on this? Are there experiments that could be run to somehow verify this knowledge separation in what is encoded?", "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)