ChorusTIC: Training-Free Multivariate Time Series Classification via Chorus In-Context Learning

Juntao Fang; Shifeng Xie; Ruichu Cai; Shengji Zheng; Zijian Li; Keli Zhang; Lujia Pan; Themis Palpanas

cs.LGAug 25, 2026

ChorusTIC: Training-Free Multivariate Time Series Classification via Chorus In-Context Learning

Authors: Juntao Fang, Shifeng Xie, Ruichu Cai, Shengji Zheng, Zijian Li, Keli Zhang, Lujia Pan, Themis Palpanas, +1 more

Organizations: Guangdong University of Technology · Huawei Noah’s Ark Lab · Université Paris Cité · Mohamed bin Zayed University of Artificial Intelligence · Shantou University

Abstract

Time series classification underpins applications in healthcare, sensing, and industrial monitoring. Although time series foundation models support forecasting and transferable representation learning, classification still typically requires fitting a task-specific classifier on each target dataset, while individual channels of multivariate inputs are often encoded independently. We introduce ChorusTIC, a classification-native foundation model for in-context classification across heterogeneous channel configurations without target-task parameter updates. ChorusTIC combines episode-consistent Random Subchannel Slot Concatenation with a shared dual-axis encoder to model temporal and cross-channel interactions and map variable channel configurations into a fixed-width representation independent of the original channel count. It then calibrates feature axes using context-derived distributions and predicts query labels through leakage-protected in-context learning. We pretrain ChorusTIC solely on synthetic labeled episodes comprising context and query sets that share a task background, with classes distinguished by sparse temporal or cross-channel rules. Evaluations on the complete UEA-30 and UCR-128 archives show strong full-context and low-label performance without target-specific classifier fitting.

Figures & tables

Figure 1: Overview of ChorusTIC. Given labeled context and unlabeled queries, the signal-level Chorus uses one RSSC channel-to-slot assignment throughout the episode. Each sampled group is processed by a shared dual-axis encoder that captures temporal structure within slots and cross-channel interactions across slots. A shared readout summarizes each encoded slot, and fixed-order concatenation yields a fixed-width representation for each sample. The task-level Chorus calibrates feature axes from context-only distributions before row-wise interaction. Finally, the leakage-protected ICL Transformer predicts each query from the labeled context while preventing direct information exchange between queries.

Protocol	Method	Target fit	Avg. Acc.	Avg. Rank
Time-series ICL	ChorusTIC	No	72.27%	3.57
Generic ICL	TabICL	No	65.33%	5.18
Generic ICL	TabICLv2	No	67.96%	4.37
Frozen TSFM	MOMENT+SVM	Yes	68.17%	5.48
	Mantis+RF	Yes	69.34%	5.22
	MantisV2+LR	Yes	70.50%	4.02

Table 1: Classification results on the complete UEA-30 archive. “Target fit” indicates whether a dataset-specific classifier is fitted on the target training split. Best and second-best average accuracies and average ranks are shown in bold and underlined , respectively. Per-dataset results are provided in Appendix D.

Protocol	Method	Target fit	Avg. Acc.	Avg. Rank
Frozen TSFM	MOMENT+SVM	Yes	77.98%	6.11
	Mantis+RF	Yes	78.67%	6.42
	MantisV2+RF	Yes	78.79%	6.51
	MantisV2+LR	Yes	80.03%	5.50
	UniShape+RF	Yes	78.86%	5.83
	NuTime+RF	Yes	69.39%	9.55

Table 2: Classification results on the UCR-128 archive. “Target fit” indicates whether a classifier is fitted on the target training split. Best and second-best results are shown in bold and underlined , respectively. Per-dataset results are provided in Appendix D.

Method	Target fit	5-shot	10-shot
MOMENT+SVM	Yes	59.42%	63.64%
MantisV2+LR	Yes	61.50%	65.80%
MantisV2+RF	Yes	60.10%	63.66%
UniShape+RF	Yes	62.19%	65.35%
NuTime+RF	Yes	54.68%	59.83%
TabICL	No	59.48%	64.06%

Table 3: Fixed-shot classification accuracy on UEA. Results are averaged over five independently sampled support sets and over the 28 and 24 datasets eligible for the 5-shot and 10-shot settings, respectively. Within each budget, all methods use the same datasets and matched support sets. “Target fit” indicates whether a classifier is fitted on the target support set. Best and second-best results are shown in bold and underlined , respectively.

Figure 2: Scaling with labeled data on UEA-30. Each point reports the average accuracy obtained using the indicated fraction of the official training split.

Factor	Variant	Avg. Acc.	$\Delta$ Acc.
Complete model	ChorusTIC	72.27%	0.00
Architecture	No channel-axis attention	71.69%	$-0.58$
	No task-conditioned calibration	70.66%	$-1.61$
Inference	No label-permutation ensemble	71.38%	$-0.89$
Pretraining prior	No cross-channel discriminative rules	71.07%	$-1.20$

Table 4: Ablation results on UEA-30.

\Delta

Acc. is measured relative to the complete model in percentage points.

Appendix figures & tables8 assets

Supplementary material from the paper’s appendix.

Appendix

Configuration	Value	Configuration	Value
Input length $L_{0}$	512	Task-level embedding width	128
Number of temporal patches $M$	32	Column-attention blocks	3
RSSC groups $G$	4	Column-attention heads	4
Slots per RSSC group $S$	4	Column inducing tokens	128
RSSC slot dimension	32	Row-interaction blocks	3
RSSC sampling strategy	Coverage	Row-attention heads	8

Appendix

Table C.1: Final ChorusTIC architecture and pretraining configuration. All values correspond to the checkpoint used for the reported UCR and UEA results.

Parameter	Final value
Checkpoint	step-6000
Evaluation mode	classifier_v2
Context mode	full training split
Label-permutation ensemble size	8
Cyclic label-permutation ensemble	enabled
RSSC inference draws $M_{R}$	4

Appendix

Table C.2: Final ChorusTIC inference configuration.

Parameter	Values considered	Final	Selection criterion
Label-permutation ensemble size $M_{\pi}$	$\{1,2,4,8\}$	8	Validation accuracy and inference cost
RSSC inference draws $M_{R}$	$\{1,2,4,8\}$	4	Validation accuracy and inference cost
Cyclic label permutation	Enabled, disabled	Enabled	Validation accuracy

Appendix

Table C.3: Development ranges and final inference hyperparameters.

Method	Official	Version or checkpoint	Target protocol
MOMENT	Yes	MOMENT-1-base	Frozen feature + SVM
Mantis	Yes	Mantis-8M	Frozen feature + RF
MantisV2	Yes	MantisV2	Frozen feature + LR/RF
UniShape	Yes	unishape_checkpoint_zeroshot	Frozen feature + RF
NuTime	Yes	checkpoint_bias9	Frozen CLS + RF
TabICL	Yes	tabicl-classifier-v1.1-20250506	In-context inference

Appendix

Table C.4: Implementations and target-task protocols of the comparison methods. “Official” indicates the use of an author-released implementation.

Item	Configuration
GPU model and count	$4\times$ NVIDIA Tesla V100 PCIe
GPU memory	32 GiB per GPU (128 GiB total)
GPU driver	570.86.15
CPU model	Intel Xeon Gold 6140 @ 2.30 GHz
System memory	251 GiB
Operating system	Ubuntu 18.04.6 LTS

Appendix

Table C.5: Computing and software environment.

Protocol	Method	Target fit	Avg. Acc. $\uparrow$	Avg. Rank $\downarrow$	W/T/L
Time-series ICL	ChorusTIC	No	72.27%	3.57	—
Generic ICL	TabICL	No	65.33%	5.18	18/3/9
Generic ICL	TabICLv2	No	67.96%	4.37	16/1/13
Frozen TSFM	MOMENT+SVM	Yes	68.17%	5.48	22/2/6
	Mantis+RF	Yes	69.34%	5.22	21/1/8
	MantisV2+LR	Yes	70.50%	4.02	18/3/9

Appendix

Table D.1: Classification results on the complete UEA-30 archive. “Target fit” indicates whether a dataset-specific classifier is fitted on the target training split. W/T/L counts are reported from the perspective of ChorusTIC. Best and second-best average accuracies and average ranks are shown in bold and underlined , respectively.

Protocol	Method	Target fit	Avg. Acc.	Avg. Rank
Frozen TSFM	MOMENT+SVM	Yes	77.98%	6.11
	Mantis+RF	Yes	78.67%	6.42
	MantisV2+RF	Yes	78.79%	6.51
	MantisV2+LR	Yes	80.03%	5.50
	UniShape+RF	Yes	78.86%	5.83
	NuTime+RF	Yes	69.39%	9.55

Appendix

Table D.2: Classification results on the complete UCR-128 archive. “Target fit” indicates whether a classifier is fitted on the target training split. Best and second-best results are shown in bold and underlined , respectively.

Dataset	TabICL	TabICLv2	MOMENT	Mantis	MV2-LR	MV2-RF	UniShape	NuTime	ChorusTIC
ArticularyWordRecognition	0.9800	0.9833	0.9600	0.9927	0.9933	0.9933	0.9900	0.7600	0.9800
AtrialFibrillation	0.2000	0.3333	0.1333	0.2800	0.1333	0.0667	0.2000	0.1333	0.2667
BasicMotions	1.0000	0.9750	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000
CharacterTrajectories	0.9847	0.9937	0.9742	0.9401	0.9742	0.9568	0.9749	0.8621	0.9889
Cricket	0.9444	0.9444	0.9861	1.0000	0.9861	0.9861	0.9722	0.8611	0.9722
DuckDuckGeese	0.2000	0.2000	0.4600	0.3880	0.4800	0.5000	0.4600	0.2600	0.4600

Appendix

Table D.3: Per-dataset classification accuracy on the complete UEA-30 archive. Best and second-best results within each dataset are shown in bold and underlined , respectively, based on the displayed four-decimal accuracies. Avg. Acc. is the macro-average over datasets; Avg. Rank is computed among the 9 displayed methods using full-precision accuracies. Abbreviations: MOMENT=MOMENT+SVM, Mantis=Mantis+RF, MV2-LR=MantisV2+LR, MV2-RF=MantisV2+RF, UniShape=UniShape+RF, and NuTime=NuTime+RF.

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TimEE: End-to-end Time Series Classification via In-Context Learning

Jaris Küken, Shi Bin Hoo, Martin Mráz, Frank Hutter+1

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Time-Series Classification Timestep Embeddings Pretraining In-Context Learning

Open paper AskPDF arXiv

Time series classification (TSC) is dominated by a two-stage paradigm: train a feature encoder -- either from scratch on the target dataset or via pretraining on large corpora -- and then fit a task-specific classifier on top. While effective, this decoupling optimizes representation learning independently of the classification objective, requires per-dataset training, and prevents the model from exploiting label information during inference. We introduce TimEE, a 4.5M-parameter foundation model for end-to-end TSC via in-context learning. Given a labeled support set and a query time series, TimEE directly outputs a predicted class distribution in a single forward pass with no per-dataset training required. Following the prior-data fitted network (PFN) framework, TimEE is meta-trained exclusively on synthetic TSC tasks, where each task contains time series with distinct class identities arising from structured distributional shifts in the generative process. Despite seeing no real time series during pre-training, TimEE ranks first in ROC AUC (and third on accuracy) on the UCR benchmark among all compared methods, which include both foundation models and supervised deep learning baselines. To our knowledge, TimEE is the first purely synthetic-pretrained model to reach state-of-the-art performance on the UCR benchmark. These results establish end-to-end ICL with synthetic priors as a compelling, largely unexplored direction for TSC, with scaling, prior design, and richer generation mechanisms as natural avenues for improvement. Code is publicly available at http://github.com/automl/timee.

Abstract

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