TIGER: Time-Series Classification with In-Context-Learning Gated Ensemble of Representations

Johann Faouzi

cs.LGOct 5, 2026

TIGER: Time-Series Classification with In-Context-Learning Gated Ensemble of Representations

Authors: Johann Faouzi

Organizations: Univ Rennes, Ensai, CNRS, CREST - UMR 9194, F-35000 Rennes, France.

Abstract

A representation family is a distinct way of extracting features from time series. Ensemble algorithms that combine several representation families remain the most accurate approach to time series classification. Current state-of-the-art ensembles, most notably HIVE-COTE2.0, pair a bespoke classification algorithm with each representation family and combine their predictions using a fixed, non-adaptive rule. We present TIGER (Time-series classification with In-context-learning Gated Ensemble of Representations), which instead applies the same small portfolio of three general-purpose classifiers (Ridge, Extra Trees, and Naive Bayes) to four representations from four distinct families, stacking the resulting twelve base learners' predictions into a meta-feature matrix. The final prediction is produced by an adaptive meta-classification rule that chooses, independently for each data set, between a weighted hard majority vote and TabICLv2, a pretrained tabular foundation model used in-context as a meta-classifier, based on the mean number of training samples available per class. On a 142-data-set benchmark drawn from the UCR time series classification archive, TIGER obtains the best mean accuracy, balanced accuracy, and F1-score among six compared algorithms, including HIVE-COTE2.0, and significantly outperforms each of the other five individually. TIGER's adaptive rule also meaningfully outperforms either of its two constituent meta-classification methods used alone, and its single hyperparameter, tuned using only a twenty-data-set development subset, is shown to generalize to the full evaluation benchmark. We further characterize TIGER's design through an extensive set of ablation experiments and report the design alternatives that we investigated and ultimately discarded.

Figures & tables

Panel of Figure 1 — Figure 1 : Overview of HIVE-COTE 2.0’s architecture.

Panel of Figure 2 — Figure 2 : Architecture of TIGER.

Panel of Figure 3 — Figure 3 : Adaptive cross-validation splitting strategy for out-of-fold predictions.

Figure 4 : Critical difference diagram for the six candidate meta-classification methods, the best base learner (picked using cross-validation), and TIGER’s adaptive rule, over the full 142-data-set benchmark, in terms of mean accuracy.

Figure 5 : TabICLv2 minus weighted-hard-majority-vote accuracy gap against training samples per class, with threshold

\tau=60.83

Data set	Mean samples/class	TabICLv2	WHMV	$\Delta$
PigAirwayPressure	2.0	0.602	0.865	-0.264
PigCVP	2.0	0.823	0.928	-0.105
Beef	6.0	0.702	0.778	-0.076
PigArtPressure	2.0	0.892	0.963	-0.071
Rock	5.0	0.839	0.883	-0.043
WordSynonyms	10.7	0.722	0.765	-0.043

Table 4 : The ten data sets on which TabICLv2 loses the most, in accuracy, compared to WHMV.

Figure 6 : Critical-difference diagram for TIGER and the five literature algorithms on accuracy, over the full 142-data-set benchmark.

Algorithm	ACC	BALACC	AUROC	NLL	F1
TIGER	0.884 (1)	0.863 (1)	0.933 (3)	2.775 (3)	0.863 (1)
KG-MTP	0.876 (2)	0.855 (2)	0.906 (4)	4.457 (4)	0.857 (2)
HC2	0.876 (3)	0.848 (3)	0.962 (1)	0.403 (1)	–
RDST	0.863 (4)	0.836 (4)	0.893 (5)	4.943 (5)	0.835 (3)
WEASEL 2.0	0.858 (5)	0.830 (6)	0.888 (6)	5.114 (6)	0.825 (5)
QUANT	0.855 (6)	0.830 (5)	0.956 (2)	0.533 (2)	0.831 (4)

Table 5 : Summary performance measures of TIGER and five literature algorithms on the full 142-data-set benchmark, ordered by accuracy. Ranks are in parentheses, the best value per column is bold, and “–” marks a metric not reported.

Algorithm	ACC	BALACC	AUROC	NLL	F1
TIGER	0.889 (1)	0.867 (1)	0.935 (3)	2.606 (3)	0.867 (1)
KG-MTP	0.884 (2)	0.862 (2)	0.911 (4)	4.186 (4)	0.865 (2)
HC2	0.882 (3)	0.855 (3)	0.963 (1)	0.387 (1)	–
RDST	0.872 (4)	0.847 (4)	0.902 (5)	4.613 (5)	0.849 (3)
WEASEL 2.0	0.871 (5)	0.845 (5)	0.900 (6)	4.654 (6)	0.844 (4)
QUANT	0.865 (6)	0.838 (6)	0.957 (2)	0.509 (2)	0.840 (5)

Table 6 : Summary performance measures on the 122 held-out data sets, ordered by accuracy. Ranks are in parentheses, the best value per column is bold, and “–” marks a metric not reported.

Algorithm	Total (h)	Median (s)	Minimum (s)	Maximum (min)
QUANT	0.26	2.0	0.3	1.1
KG-MTP	2.08	14.4	1.2	10.4
RDST	3.11	19.3	1.0	13.8
WEASEL 2.0	5.94	24.8	1.6	43.2
TIGER	20.65	145.4	19.0	213.2
HC2	149.25	834.7	55.6	1469.9

Table 7 : Combined training-plus-inference wall-clock runtime for TIGER and five literature algorithms, ordered by median runtime (lowest in bold).

Algorithm	Total (h)	Median (s)	Minimum (s)	Maximum (min)
QUANT	0.25	1.6	0.2	1.1
KG-MTP	0.98	5.8	0.2	9.3
RDST	1.52	8.4	0.2	9.9
WEASEL 2.0	2.90	11.6	0.8	17.3
TIGER	10.90	76.3	15.2	114.4
HC2	100.86	418.0	17.6	1331.4

Table 8 : Training wall-clock runtime for TIGER and five literature algorithms, ordered by median runtime (lowest in bold).

Algorithm	Total (h)	Median (s)	Minimum (s)	Maximum (min)
QUANT	0.02	0.1	0.0	0.1
KG-MTP	1.10	7.1	0.4	6.5
RDST	1.58	8.0	0.3	11.9
WEASEL 2.0	3.04	8.7	0.4	39.3
TIGER	9.76	39.5	1.8	135.7
HC2	48.34	248.2	14.7	361.5

Table 9 : Inference wall-clock runtime for TIGER and five literature algorithms, ordered by median runtime (lowest in bold).

Algorithm	Learner	$k$ -fold	Bag(15)	Bag(25)	Native OOB
KG-MTP	Ridge	0.0327	0.0347	0.0334	–
KG-MTP	Extra Trees	0.0357	–	–	0.0390
KG-MTP	Naive Bayes	0.0546	0.0503	0.0494	–
QUANT	Ridge	0.0360	0.0403	0.0383	–
QUANT	Extra Trees	0.0383	–	–	0.0407
QUANT	Naive Bayes	0.0431	0.0535	0.0520	–

Table 10 : Mean absolute gap between each out-of-fold estimator’s accuracy and the held-out test accuracy, per (family, learner) pair. Smaller is more honest, and the best value per row is bold.

Algorithm	Learner	$k$ -fold	Bag(15)	Bag(25)	Native OOB
KG-MTP	Ridge	44.1	171.1	282.5	–
KG-MTP	Extra Trees	11.1	–	–	54.8
KG-MTP	Naive Bayes	25.8	217.8	360.0	–
QUANT	Ridge	4.1	15.9	26.6	–
QUANT	Extra Trees	5.8	–	–	8.0
QUANT	Naive Bayes	1.7	14.7	24.4	–

Table 11 : Mean wall-clock seconds per unit for each out-of-fold estimator, per (family, learner) pair (fastest in bold).

Data set	Classes	Native	ECOC	Native	ECOC
		Accuracy		Runtimes (s)
EOGHorizontalSignal	12	0.814	0.818	132	886
EOGVerticalSignal	12	0.771	0.762	131	880
FacesUCR	14	0.957	0.962	361	1557
GestureMidAirD3	26	0.506	0.511	150	972
PigAirwayPressure	52	0.548	0.526	291	1248

Table 12 : TabICLv2’s native many-class handling vs. its ECOC-wrapped variant, on the six development data sets with more than ten classes (runtimes in seconds, best accuracy per row in bold).

Method	Mean accuracy	Gap to six-method oracle
Best single base learner (CV-picked)	0.8778	0.0094
TIGER’s adaptive rule (deployed)	0.8838	0.0034
WHMV / TabICLv2 oracle (hindsight)	0.8869	0.0003
Six-method oracle (hindsight)	0.8872	0.0000
Best single base learner (test hindsight)	0.8922	–

Table 13 : Mean accuracy of TIGER’s deployed rule against four reference points (red rows use test-set hindsight and cannot be deployed).

Method	Data sets won	Mean margin	Max margin
CAWPE	19	0.0013	0.0103
Hard majority vote	17	0.0005	0.0026
LightGBM	10	0.0002	0.0013
Logistic regression	6	0.0018	0.0082

Table 14 : Per-method breakdown of the six-method oracle’s gain over Table 13 ’s two-method oracle, for the four methods TIGER does not deploy. “Data sets won” counts the data sets where that method ties or leads all six candidates. “Margin” is its own accuracy minus the two-method oracle’s, on the data sets it wins.

Algorithm	Mean accuracy	Mean balanced accuracy	Data sets won
KG-MTP	0.8661	0.8437	75
RDST	0.8577	0.8301	30
WEASEL 2.0	0.8432	0.8148	27
QUANT	0.8328	0.8077	21

Table 15 : Each representation family’s standalone predictive strength before stacking, on 139 of 142 data sets (best per column in bold).

Classes	Data sets	TIGER’s adaptive rule	Six-method oracle	Gap
2 (binary)	50	0.9291	0.9307	0.0016
3–5	38	0.8905	0.8930	0.0025
6–10	30	0.8478	0.8532	0.0054
11+	24	0.8241	0.8297	0.0057

Table 16 : Meta-oracle headroom broken down by number of classes.

Training samples	Data sets	TIGER’s adaptive rule	Six-method oracle	Gap
$\leq$ 60	36	0.9447	0.9469	0.0022
61–217	35	0.8347	0.8378	0.0031
218–466	36	0.8273	0.8339	0.0066
$\geq$ 467	35	0.9285	0.9299	0.0014

Table 17 : Meta-oracle headroom broken down by training set size, binned at the quartiles.

Data set	Mean samples/class	TabICLv2 vs. oracle	$\geq\tau$
PhoneHeartbeatSound	84.8	+0.072	✓
EthanolLevel	126.0	+0.072	✓
SemgHandMovementCh2	75.0	+0.061	✓
ScreenType	125.0	+0.044	✓
ChlorineConcentration	155.7	+0.042	✓
SemgHandSubjectCh2	90.0	+0.041	✓

Table 18 : The ten data sets on which TabICLv2, used alone as the meta-classifier, gains the most accuracy relative to the WHMV/TabICLv2 oracle (checkmark: at or above

\tau=60.83

Figure 7 : TabICLv2’s mean accuracy per family as a function of reduced feature count. “default” is each family’s own unconstrained reduction.

Figure 8 : TabICLv2 at its best reduced-feature tag vs. each family’s original algorithm on raw features.

Appendix figures & tables6 assets

Supplementary material from the paper’s appendix.

Appendix

Data set	Training	Test	Classes	Length	Mean samples/class
ACSF1	100	100	10	1460	10.0
AconityMINIPrinterLarge	2403	1184	2	300	1201.5
AconityMINIPrinterSmall	589	292	2	300	294.5
Adiac	390	391	37	176	10.5
AllGestureWiimoteX	300	700	10	500	30.0
AllGestureWiimoteY	300	700	10	500	30.0

Appendix

Table 20 : The 142 data sets of the full evaluation benchmark (mean samples/class computed on the training set).

Data set	HMV	LR	LightGBM	CAWPE	WHMV	TabICLv2
ACSF1	0.866	0.857	0.855	0.862	0.871	0.873
AconityMINIPrinterLarge	0.954	0.963	0.964	0.953	0.956	0.967
AconityMINIPrinterSmall	0.975	0.978	0.975	0.975	0.975	0.978
Adiac	0.811	0.828	0.814	0.823	0.821	0.852
AllGestureWiimoteX	0.745	0.758	0.755	0.778	0.771	0.780
AllGestureWiimoteY	0.754	0.780	0.786	0.798	0.785	0.804

Appendix

Table 21 : Mean accuracy (over 30 resamples) of the six candidate meta-classification methods, for each of the 142 data sets (HMV = hard majority vote, LR = logistic regression, WHMV = weighted hard majority vote).

Algorithm	ACC	BALACC	AUROC	NLL	F1
TIGER	0.895 (1)	0.862 (1)	0.955 (1)	0.265 (1)	0.860 (1)
KG-MTP	0.879 (2)	0.839 (2)	0.889 (4)	4.374 (4)	0.842 (2)
HC2	0.878 (3)	0.825 (4)	0.950 (2)	0.350 (2)	–
QUANT	0.874 (4)	0.831 (3)	0.948 (3)	0.402 (3)	0.832 (3)
RDST	0.863 (5)	0.813 (5)	0.867 (5)	4.952 (5)	0.807 (4)
WEASEL 2.0	0.855 (6)	0.801 (6)	0.858 (6)	5.240 (6)	0.788 (5)

Appendix

Table 22 : Summary performance measures on the 57 data sets where TIGER’s adaptive rule always selects TabICLv2.

Data set	ACC	BALACC	AUROC	NLL	F1
ACSF1	0.871	0.871	0.928	4.662	0.868
AconityMINIPrinterLarge	0.967	0.964	0.988	0.115	0.951
AconityMINIPrinterSmall	0.978	0.966	0.982	0.101	0.960
Adiac	0.821	0.825	0.908	6.468	0.813
AllGestureWiimoteX	0.771	0.771	0.873	8.266	0.768
AllGestureWiimoteY	0.785	0.785	0.881	7.734	0.783

Appendix

Table 23 : TIGER’s mean ACC, BALACC, AUROC, NLL, and F1 (over 30 resamples), for each of the 142 data sets.

Figure 9 : Reliability of the Platt-calibrated Ridge meta-features, per representation family. The dashed diagonal marks perfect calibration.

Algorithm	Expected Calibration Error
WEASEL 2.0	0.0440
KG-MTP	0.0443
RDST	0.0453
QUANT	0.0583

Appendix

Table 24 : Expected calibration error of the Platt-scaled Ridge meta-features, per representation family (lowest in bold).

Explore similar work

In-Context Time Series Classification with Random Convolutional Features
Jul 21, 2026Joscha Cüppers, Jilles VreekenTabular Foundation Models Time Series Classification
CISPA Helmholtz Center for Information Security
TimEE: End-to-end Time Series Classification via In-Context Learning
Jul 8, 2026Jaris Küken, Shi Bin Hoo, Martin Mráz +2Synthetic Data Pretraining Time Series Classification
University of Freiburg · Zuse School ELIZA Darmstadt · Prior Labs +1
RocketPFN: Accurate Time Series Classification via In-Context Learning
Jun 19, 2026Franco Martino O'Rourke, Ana Trisovic, Dimitris BertsimasTime Series Classification Classification

Jul 21, 2026, cs.LGJ/K move · Enter open · S save

In-Context Time Series Classification with Random Convolutional Features

Joscha Cüppers, Jilles Vreeken

CISPA Helmholtz Center for Information Security

Tabular Foundation Models Time Series Classification Tabular ICL

Open paper AskPDF arXiv

Time series classification is central to domains such as medical signal analysis, industrial monitoring, and sensor-based activity recognition, where class information manifests as localized shapes, specific frequencies, temporal shifts, or complex cross-channel interactions. Random convolutional transforms capture these diverse patterns by converting time series into rich, fixed-dimensional feature representations that can be processed by standard tabular classifiers. While these representations are traditionally paired with simple linear models, we investigate whether a pretrained tabular foundation model can exploit them more effectively and how its performance depends on the available data and inference budget. We propose MASHT, a pipeline that combines MultiRocket and Hydra features with an in-context tabular foundation model. Our approach uses a pretrained tabular foundation model to bypass task-specific model training, requiring only feature extraction and direct inference. Extensive experiments demonstrate that MASHT matches state-of-the-art time series classification baselines on univariate tasks, achieving a lower average rank than HIVE-COTE 2.0. On multivariate datasets, MASHT remains highly competitive with the strongest reference methods. Controlled resource experiments show that compact feature tables retain most of the accuracy at substantially lower runtime, while TabPFN outperforms a matched linear baseline across the evaluated label budgets on univariate tasks. These results highlight practical trade-offs between predictive performance, labeled data, and inference cost.

Abstract

Explore similar work

In-Context Time Series Classification with Random Convolutional Features

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

RocketPFN: Accurate Time Series Classification via In-Context Learning