MambaSL: Exploring Single-Layer Mamba for Time Series Classification
Authors: Yoo-Min Jung
Leekyung Kim
Venue: ICLR 2026 Poster (2026)
Keywords: time series classification
single-layer Mamba
modular selective SSM
multi-head adaptive pooling
skip connection
Primary area: learning on time series and dynamical systems
TL;DR: We introduce MambaSL, a minimally redesigned single-layer Mamba that achieves state-of-the-art accuracy on the UEA30 benchmark, with reproducible evaluation covering all baselines.

Abstract: Despite recent advances in state space models (SSMs) such as Mamba across various sequence domains, research on their standalone capacity for time series classification (TSC) has remained limited. 
We propose MambaSL, a framework that minimally redesigns the selective SSM and projection layers of a single-layer Mamba, guided by four TSC-specific hypotheses.
To address benchmarking limitations—restricted configurations, partial University of East Anglia (UEA) dataset coverage, and insufficiently reproducible setups—we re-evaluate 20 strong baselines across all 30 UEA datasets under a unified protocol. 
As a result, MambaSL achieves state-of-the-art performance with statistically significant average improvements, while ensuring reproducibility via public checkpoints for all evaluated models.
Together with visualizations, these results demonstrate the potential of Mamba-based architectures as a TSC backbone.

Published as a conference paper at ICLR 2026
MAMBASL: EXPLORING SINGLE-LAYER MAMBA FOR TIME SERIES CLASSIFICATION
Yoo-Min Jung & Leekyung Kim
Department of Industrial Engineering, Seoul National University pame 738 4 com, k k1k97@anu. ww...ac............ ac.kr kr
ABSTRACT
Despite recent advances in state space models (SSMs) such as Mamba across various sequence domains, research on their standalone capacity for time series classification (TSC) has remained limited. We propose MambaSL, a framework that minimally redesigns the selective SSM and projection layers of a single-layer Mamba, guided by four TSC-specific hypotheses. To address benchmarking limi- tations-restrictt configurations, partial University of East Anglia (UEA) dataset coverage, and insufficiently reproducible setups we re-evaluate 20 strong base- lines across all 30 UEA datasets under a unified protocol. As result, Mam- baSL achieves state-of-the-arr performance with statistically significant average improvements, while ensuring reproducibility via public checkpoints for all eval- uated models. Together with visualizations, these results demonstrate the potential of Mamba-based architectures as a TSC backbone.
INTRODUCTION
State space models (SSMs) have emerged as a strong alternative Transformers (Vaswani et al. 2017), with Mamba (Gu & Dao 2024 Dao & Gu 2024) marking a milestone in sequence domains time series, however, convolutional- and Transformer based architectures still dominate, with the former excelling in time series classi- fication (TSC) and the latter in time series forecasting (TSF) (Wang et al.] (024b). While early works such as TimeMachine (Ahamed & Cheng. 2024 and S-Mamba (Wang et al. 2025b showed Mamba's s promise in TSF, its role TSC remains underexplored.
such as language and video (Lenz et al. 2025 al.
Two gaps motivate our study. First, the standalone capacity of Mamba for TSC has seen little investigation. Waag et al. (2024b) ranked Mamba as the weakest TSC backbone, likely due to a lack of research rather than inherent architectural limitations, as only the vanilla variant was evalu- ated. To the best of our knowledge, TSCMamba (Ahamed Cheng 2025) is the only subsequent benchmarking attempt for TSC. However, its integration of feature engineering techniques such as ROCKET (Dempster et al. 2020) and CWT (Mallat) 1999) obscures Mamba's 's intrinsic contribution.
As second gap. current TSC henchmarking suffers from three critical issues related to coverage, fairness, and reproducibility. First, evaluations are often restricted to only a fraction of the Univer- sity of East Anglia (UEA) archive (Bagnall et al. 20118). leaving out challenging datasets with long sequences or high input dimensionality. Second, non-TSC models are frequently adopted without proper re-tuning, which risks underestimating their capacity; for example, TSF models like DLin- ear (Zeng et al. 2023) and PatchTST (Nie et al., 2023) have been reported in TSC contexts using default settings Wu al., 2023 Luo & Wang 2024h. Finally, reproducibility remains concern e.g., re-evaluations of VS2 (Yue et al.) (022) and GPT4TS (Zhou et al., 2023) by Eldele et al. (2024) revealed average accuracy drops exceeding 9%p compared to their original reports. These limitations undermine the reliability of comparative conclusions in the literature.
Motivated by these gaps, we revisit Mamba as a TSC backbone from two perspectives. (i) Archi- tecture: We propose four TSC-specific hypotheses (HI-H4), which guide the redesign of selective SSM components and the input and output projection layers. (ii) Evaluation protocol: We establish consistent benchmarking setup and re-evaluate strong TSC baselines across all 30 multivariate UEA datasets with extensive hyperparameter sweeps. Under the protocol, our single-laye Mamba TSC framework, MambaSI., achieves state-of-the-art performance. Our key contributions are:

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Published as a conference paper at ICLR 2026
TSC-specific hypotheses and architectural refinements. We propose the following four hy- potheses (HI-H4) that explain why naively applying Mamba within the existing TSC pipeline does not fully realize its potential, and validate them through corresponding design adjustments:
(H1) Scale input projection. Since Mamba's output modulated by gating unit (Hua al.) 2022 Mehta et al. 2023a), insufficient input context can bottleneck performance, moti- vating a larger input projection receptive field for densely sampled time series.
(H2) Modularize time (in)variance. As time series often exhibit near linear time-invariant be- havior, we decouple time variance of Mamba as a hyperparameter. Simpler configurations often perform better, contradicting the ablation results from Gu & Dao (2024).
(H3) Remove skip connection. In shallow networks, skip connections yield minimal perfor- mance gains (He et al. 2016 Ismail Fawaz et al. (020). Given Mamba' 's strong long-range memory, we remove skip connections and construct logits solely from hidden states.
(H4) Aggregate via adaptive pooling. Time series classification spans both global and event- driven patterns, which conventional pooling cannot accommodate, We therefore propose a multi-head adaptive pooling that weights temporal features in a dataset-specific manner.
Comprehensive and reproducible UEA benchmarking, We evaluate 20 models on all 30 UEA datasets, covering various sequence lengths (8-17,984), input dimensions (2-1,345), and sample sizes (12-25,000), For each model, explore approximately 200 hyperparameter combinations to select the optimal configuration Notably, TSF models previously tested on the UEA datasets showed an average accuracy improvement of 3. 3.04%p with hyperparameter tuning alone.
Empirical validation of a single-layer Mamba. Our MambaSL framework achieves the state- of-the-art performance across the UEA benchmark, outperforming the second-best method by 1.41%p. We demonstrate the inherent potential of Mamba by validating our hypotheses through ablation studies and highlighting backbone- and dataset specific features through visualizations.
The remainder of the paper is organized as follows. Section 2 reviews related work for TSC and Mamba, section 3 details our hypotheses and architectural refinements, section Adescribes our ex- perimental setup and implementation details, section 5 presents results and analysis, and section 6 concludes the paper.
RELATED WORK
Recent advances in deep learning (DL) for TSC can be grouped into three streams. (i) TSC-specific
models such as TS2 Vec tailor encoders and readouts directly for classification Yuue et al. 2022
Eldele et al. 2024: Wen et al. 2025). (ii) Foundation models aim to adapt a single model across
multiple time-series tasks, inc including TSC (Wu et al. 2023 Zhou et al., 2023 Luo & Wang 2024 Wang et al. 2025a). (ii) ToF-origin models are frequently repurposed as baselines for TpC-specific
and foundation models (Zeng et al. 2023 Zhang & Yan 2023 Nie et al., (023). Although evalu- ations typically rely on the UEA benchmark, inconsistent use of dataset subsets and single-setting evaluations for ToF-origin models have led to underestimated baselines. This highlights the need for standardized and consistent benchmarking in TSC.
Within SSMs, Mamba has been explored primarily for TSF. Variants such as TimeMachine (Ahamed Cheng 2024) and S-Mamba (Wang et al.) 2025b) typically construct temporally mixed embed- dings and then apply bidirectional Mamba along the channel axis to mitigate scan-order sensitivity. By contrast, adaptations to TSC in general are comparatively scarce, with TSCMamba (Ahamed & Cheng, 2025) being the only variant. Across TSF and TSC alike, Mamba variants tend to update the SSM state along non-temporal axes (channel or frequency) rather than the original time axis.
METHODS
3.1 PROBLEM DEFINITION
Let X = L1.L Iii...: : ] be a multivariate time series of length L, where each 2; at time t is
-D vector. TSC infers the label y e {L...., dy from X, where dy the number of classes.
Many TSC architectures consist of three fundamental modules: an input projection 인, that maps the
input subsequence 1+t (receptive field of size k) into -D vector ã; [-((1] (1)
. . (dm))
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feature extractor eFF that produces per-timestep feature vectors fILL from Z1,4 (typically of the same size), and an output projection &CLF that aggregates fi.z into logits I E R Formally,
c; क (ate
fIL L FF (#1:L
CLL (f:L);
(3)
y arg max softmax(I)
(l,,,,))))
The aggregation function of #CLF can be instantiated as fully connected layer, global average/max pooling, last-timestep readout. Whereas many recent studies primarily emphasize improving PFE, we examine and improve the entire TSC pipeline for Mamba in section 3.3
3.2 TIME-VARIANT SSM IN MAMBA
At a high level, Mamba can viewed as a selective SSM core augmented with lightweight gated linear block (Hua et al. [222] Mehta et al.) 2023a). We next review the selective SSM, clarifying its components and establishing the notation used our proposed method (section 3.3.2).
3.2.1 LINEAR TIME-INVARIAN SSM
An SSM maps signal (l) R dg-D state vector sl) and projects it onto output y(1) R:
(t) = As(t) + Bu(t),
y(t) Cs(() + Du(t),
(5)
with four parameters, A E xd. B [dxx1 C 1xxd, and D E R. This yields linear time-invariant (LTT) system when the parameters are fixed over time.
With step size A 0, the system is discretized as
8, = As:- 1 + Buc,
Cs: + Du.,
(6)
where Ā A) and B A, are discretization rules. Different discretization rules exist; the zero-order hold (ZOH) method is discussed in section 3.2.2
While these forms are standard, we retain them here as the basis for our multivariate extension. In SSM-based DL models, this extension is typically implemented by sharing A across channels and broadcasting the other parameters (Gu al. 202T: 2022, Gu & Dao 2024). For instance, when
using the SSM as the feature extractor PPE in equation 2 each input channel =((") is mapped to
d,-D state via channel-specific B(j) (and step size ( and shared A. Thus, at time t, the hidden
(A(A)) A, B6) )(()) The feature vector f: is also generated by channel-specific C and D6), That is, in DL practice, the multivariate SSM is fundamentally channel-inee -ineeenednndeeedeett LTI system.
state S, [ ........ ; (dmm) E Rd, xdm where o(") (A((),, A)s
To our knowledge, however, existing works rarely formalize how A, B, and C operate across chan- nels. We show explicitly that A primarily acts along the temporal axis each channel, whereas B and primarily serve as channel- -wiee projections under a shared A Mamba's 's selective SSM. This clarification, often left implicit prior literature, the stage for the following section.
3.2.2 SELECTIVE SSM
Mamba (Gu & Dao 2024 proposed selective SSM that allows A, B, and C to selectively prop- agate or forget information from given input. Given the projected input क; Rdd three Iearnable maps, Φa. ΦB, and Φc. produce the input-dependent parameters A., B,, and C₁, respectively;
A Φ사 (wt, <( [Linearbia bias [Lineara, (#x) = (A dm
(dw))
E IR
B. Φg Linear (xt)
1d
C. ( Linear (T:)
E 1xd
Here, Ç is the softplus activation, Linearhing bins (x:) and Linear,(x) denote linear projections into d (identified with ddx1) with and without bias, and d, the rank of low-rank projection for A,.
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The selective SSM is then formulated as follows
(5) A (i) (i)
St-1
B Ft
(() f(
C.s (3)
+ D
j
d.
(8)
where A.") and _, are discretized by the ZOH rule (Mehta et al. 2023b
Gu & Dao 2024):
A FA(A,) A) exp(A)) A);
B = FB(A,) A. B.) (A ( A) M) B,.
This transformation changes the SSM from LTI time-variant (TV) system, enabling context- aware reasoning that selectively stores extracts key information. The explicit formulation also highlights that A, Bt, and C₁ shared across channels, while A. and B,d)) differ due to the chsnnel-wise A
3.2.3
ON TIME VARIANCE IN A, B, AND C
TIAA
TV-A
TI-B
TV-B
4 4
4
4
A
g
Wn
B:
D B,
A A A
Rf
R1
Figure 1: TI/TV parameterization of (left) A and (right) B in the SSM. TI-A fixes the update rate, while TV-A adapts it to align sequences with varying speeds. TI-B preserves channel independence, whereas TV-B introduces input-dependent mixing. follows the same pattern as B at the output stage, highlighting the temporal pacing of A versus the spatial routing of B and C.
While the parameters in Mamba share the same mechanism to yield a TV system by default, as defined in equation 7 their main roles differ. We denote A, B, and C in the LTI SSM using TI- notation (section 3.2.1) and A., B., and C, in the selective SSM using TV- notation (section 3.2.2
A: Temporal Update Rate. A controls the timescale of state updates. TI-A keeps constant rate, whereas TV-A adapts to context: larger A accelerates updates, and smaller A prolongs memory. This resembles dynamic time warping (DTW), aligning sequences with varying local speeds (see the left side of Figure
B: Input-to-State Routing. B determines how each input channel drives the latent state. TI-B enforces channel-in -innueeliddeeendent routing, whereas TV-B enables context-dependem mixing of input channels before entering the state space (see the right side of Figure 1
C: State-to-Output Readout. maps states output features. TI-C applies fixed readout, preserving channel independence. TV-C introduces adaptive mixing at the output stage, potentially capturing richer cross-channel interactions.
Interplay. TV-A governs temporal dynamics, whereas TV-B and TV-C govern spatial mixing- The extreme cases are:
TI-A, TI-B, TI-C: LTI system with channel-indeppnddnt I/O;
TV-A, TV-B, TV-C: fully TV system with channel-mixed I/O.
Thus, temporal and spatial variability
be decoupled and selectively controlled, motivating second hypothesis (H2) in section 3.3.2 Note that, while these roles are primary, the ZOH rule
couples them some extent: A indirectly modulate the influence of B and C, and vice versa.
3.3 SINGLE-LAYER MAMBA FOR TSC
We refine the vanilla Mamba and projection layers through four hypotheses (H1-H4) motivated by their limitations in the standard TSC pipeline. The full framework is shown in Figure 2
For simplicity, we treat the selective SSM without shift as the feature extractor itself, using ã; in place of and fü in place of from equation 6 In practice, A, B, and C are generated from expanded input after linear projection and local convolution, but we omit this detail keep the notation concise.
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Output
SSM
Output Projection
per-time logit
per-time
Weight
Softmax
Linear
Adaptive Max Pooling
Dropout
Modular Selective SSM
Linear
Linear
Conv1D
Linear
Linear
LayerNorm
Dropout
MambaBlock
Positional Encoding
SiLU
Input Projection
Depends on time variability hyperpurumeters (0g, Gz. 0g)
CD
Input
Leamable parameter
Figure 2: Overall structure of MambaSL, a single-layer Mamba framework designed for TSC.
3.3. INPUT PROJECTION
H1: Scale input projection In recent time series models, O is implemented a 1-D convolution
with a fixed kernel size k = 3 (Wu et al.) 2023 Zhou et al. (002)). unless alternatives such as patching or frequency-doman transforms are employed NNe al. 2023 Wang et al.) 2025a).
Since Mamba's 's gating mechanism modulates the SSM output based on this projection, we hypoth- esize that longer sequences require proportionally larger receptive fields. We therefore define
k = max [AL]),
(10)
with kimin (minimum k:), A 0.02 (sequence ratio), and stride to isolate kernel-size effects.
3.3.2 FEATURE EXTRACTOR
We adopt the vanilla Mamba feature extractor, introducing minimal changes its SSM core under two hypotheses: (H2) modularizing time (in) and (H3) removing the skip connection.
H2: Modularize time (in)variance Building on section 3.2.3 we hypothesize that the optimal TI/TV configuration of A, B, and C' is datasct-dependent. Evidence from TSF shows that chan- nel independence may outperform mixing (Zeng et al.) 2023 Nie et al. 2023), and shape-based distance- akin to with channel mixing is inevitable at the output, we expect C' to have comparatively smaller effect, while A and B are likely to exert a larger dataset-specifie influence.
can rival DTW in TSC (Paparrizos & Gravano 2015). As aggregation
To systematically examine this, we introduce binary switches 0A,0B. ; Og whether each parameter follows TI TV form. For channel j E {l...., dm3 and time t, the effective parameters are
{0, 1} that determine
A000 (1 -1-))) 0s (1) =11 - 02)
A0) 40)
0s ds (.:)
Ryo,
B(5)' =(1 (1-0p) ( +0 B, = -1-0g)) Bú) + +0 (B(
E Rdxx
(11)
" =(1 (1- (1- ( fc C. (1) (() + lc (
E ERIxd.
where all defined in equation This modularization yields 2 configurations, ranging
from a fully LTI system (all 0) to the selective SSM (all 0 = 1). Substituting equation [1 into
equations 8 and produces modularized selective SSM, illustrated in the upper left of Figure]
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H3: Remove skip connection Skip (residual) connections improve optimization in deep networks, yet their effect diminishes in shallow networks (He et al. 2016 Kim et al. 2017). Inception (Is- mail Fawaz et al., (2020) further shows that the skip connection makes little difference across 85 UCR datasets (Chen et al. 2015), indicating that it is not always essential for TSC.
In the single-layer setting, such shortcuts may bypass the SSM and hinder Mamba's representation learning. We therefore hypothesize that removing the skip connection forces the model to rely solely on the SSM's state evolution. This is implemented by omitting the D(i) term in equation g
C
(no skip term).
(12)
This modification positions learning the state vector sf the core of TSC.
3.3.3 OUTPUT PROJECTION
Aggregate via adaptive pooling After the Mamba block, per-timestep features are aggregated into a logit vec- tor L. Conventional pooling methods, such as average or max, treat all steps equally or rely on a single dominant one, thus ig- noring data-specific temporal importance. This issue is partic- ularly critical for recurrent models, where the predicted label may shift over time (e.g., Handwrit sequence labeled g may initially resemble class a before later aligning with g).
H4:
fu
Linear
Linear
h
max
pa--imc Bagt
softmax
We propose multi-head adaptive pooling with learnable gates (Figure 3. Specifically, Ny, independent gating heads each produce scalar score 9t.h for time step
pa-sam woidh
9r,h = ww ft + bb, h= I...,. No.
(13)
Figure Illustration of proposed multi-head adaptive pooling with (L, dm, Nr, dyy = (7,
For each 1, the maximum gate value across heads is selected and normalized via softmax to obtain per-timestep weight ags which is then applied to the per-timestep logit vector L,:
exp(x))
gt max It
1=
Σ ad4
(14)
E exp(gx)
This formulation generalizes conventional pociiionnooooooongoggoo uniform ay recovers averaging, while a sharply peaked a; approximates max pooling. Compared attention pooling (Bahdanau et al. 2016). our design is lightweight yet expressive: multi-head gating explores diverse patterns, while adaptive max pooling selects the most confident signals, enabling robust dataset-specific aggregation.
4 EXPERIMENT
We conduct experiments on the full UEA benchmark (Bagnall et al. 2018), covering diverse se- quence lengths, input dimensions, and sample sizes. For conciseness, we will hereafter refer each dataset by its code (e.g., EC for FihhnnlCConcentration. see 4). To ensure fairness, we allocate an identical search budget per tion for each dataset. Detailed descriptions of the environment, datasets, and metrics are provided in appendix A and hyperparameter settings are provided in appendix B
under unified protocol, selecting the best configura-
Baselines We include (1) non-DL methods: DTW-based nearest neighbor (DTW pi Berndt & Clif-
Ford 1994), ROCKET (Dempster et al., 2020), HIVE-COTE 2.0 (HC2; Middlehurst et al.) 2021). l).
Hydra (Dempster et al. 2023),, Multioocket (MR: Tan et al. 2022) +Hydra; (2) MLP-based models:
DLinear (Zeng et |l.] (023). LightTS (Zhang et 2022). MiS-Mixer (Li ct al. 2023); (3) CNN-
based models: NetesNet (Wu al. 2023). ModemTTN (Luo & Wang 2024). TSLANet (Eldele
et al.
2024). TimeMixer+ (Wang et al. (202a)); (4) Transformer-basee models: FEDformer (Zhou
2022). ETSformer (Woo
(002). Crossformer (Zhang & Yan (023). PatchTST (Nie
et al.
2023), GPT4TS (Zhou et al.) 2023). iTransformer (Liu et al. [2024]; (5) shape-based model:
InterpGN (Wen al. 2025); and (6) Mamba-based model: TSCMamba (Ahamed Cheng 2025)
Whiile Mamba and related literature typically derive the SSM equation without the skip connection D. their official implementations default enabling (statc-spaces 2025 We changed this be tunable.

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5
RESULTS AND DISCUSSION
5.1
CLASSIFICATION RESULTS ON THE UEA BENCHMARK
Non-DL
MLP-based
CNN-based
Transtormerbaseed
Shape-based
Mamba-based
10 dataset
10 dataset
10 dataset
10 dataset all dataset
X
10 dataset
X 10 dataset all dataset
all dataset
all dataset
all dataset
all dataset
awerna accurst (%))
20 40
27
20%
25,72
72-45
25.93
02
70:12
.0
6B
25
10
10..
Soneooo rank
10
0-5
DTW_D 0
ROCKET
HC2
Hydra
MR MR+Ykydra
DLinear
Lightrs
NTS Mixer
HTS-Mixer
Timestef
ModerTTC
TSLANet
Timelliee++
PEDrormer
ETStormer
Crossformer
PatchTST
GPT4TS
manstforer
InterpGN
rSCMamba
MambeSt
Figure 4: Comparison of average classification performances on the UEA benchmark. Non-DL mod- els include DTW-D, ROCKET, HC2, Hydra, and MR+Hydra; DL models are grouped by their back- bone structures.
As shown in Figure achieving the best average accuracy and rank across both the widely used 10-dataset subset and the full set of 30 UEA datasets. In particular, MambaSL surpassed the second-best model, TSC- Mamba, by an average margin of 1.41 4.p. Wilcoxon signed-rank tests (Wilcoxon) 1945) across the 30 datasets confirmed statistically significant gains (p < 0.05) over all models except HC2 (p 0.56), despite HC2 ranking only 8th overall (see appendix C.I for full results and statistics).
proposed Malell-MamaSSS-eesttt
new state of the art by
As detailed in appendix C.T. MambaSL tends to perform strongly on datasets where multi-scale con- volutions or vequency-domai features are typically dominant. For example, AWR, CR, EW, EP, HW, PS, RS, UW, and SWJ generally favor non-DL., TSLANet, InterpGN, and TSCMamba, yet MambaSI. remains competitive. In particular, for CR and HW, MambaSL is the only DL model that surpasses DTW At the same time, MambaSL shows competitive results on datasets such as FD, HMD, NATO, and SRS1, where MLP- or Transformer- based backbones have relative advantages. These results, achieved without additional domain transforms or complex mechanisms, demonstrate both the in- herent capability of Mamba and the effectiveness of our architectural refinements.
To further contextualize the results, Figure 5 visualizes each model ing a 2-D embedding of its 30 accu- racy values via uniform manifold ap- proximation and projection (UMAP) (McInnes et al. 2020p. DL and non- DL methods are clearly clustered in separate regions, while InterpGN and MambaSI appear in between. The placement of InterpGNN reasonable since it ensembles shape-based based and DL models. This suggests that Mam- baSL shares the strengths of both DL and non-DI DL approaches.
Visualization of TSC models using UMAP (n_ neinhhoorss11,, metric=correlation)
iTransformer ModemTTN
TimeMixer+ 'MSS-Mixer
Crossformrr
LightTS
GPT4TS
DLinear TimesNet
PatchTST FEDformer
ETStormer
MambaSI TSLÄNat
TSCMamba
InterpGN
DTW.
HC2
Hydra
ROCKET
MR+Hyddr
component
Figure 5: Visualization of the UEA classification results of each TSC model using the UMAP algorithm.
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To complement the model-level anal-
Visualization of UEA dalasets using UMAP (n_neighbors==9 metric=coorelation)
ysis Figure [ we visualize the UEA datasets
PS
UMAP in Fig- ure 6 Here, each point represents dataset embedded by its 21 curacy values. Compared to the full results in appendix C.I. this view clearly shows the pattern of non-DL methods; datasets on which all non- DL baselines perform poorly form tight cluster in the lower right (FD, JV, SR52, AF, FM, HMD, MT, SWJ), whereas the upper-left cluster tends to outperform under the non-DLs.
LSST
RS
CR
ognemmett
UW
AWR
IW
EP
SAD
LiB
SRS1
PD
PEMS
DÒG
HB
NATO
JV
FM
FD
sw]
MI
HMD SRS
Figure 6: Visualization of the UEA classification results us- ing the UMAP algorithm along dataset axis.
Finally, reproducibility remains a challenge DL models due to randomness in initialization and optimization. We therefore compared our results with prior reports (appendix C.3 Our evalu- ation yielded higher performance than previously reported results, except for Modem1TN and TimeMixer++. However, their average ranks decline when the reported results are applied, con- firming the fairness and rigor our protocol across all baselines. For ModernTCN, although the average accuracy was lower than expected, we achieved higher accuracy with fewer hyperparameter trials compared to the revisited study (Akacik & Hoogendoorn| (025). Notably, TSF-origin mod- els (DLinear, LightTS, MTS-Mixer FEDłormer, ETSsformer, Crossformer, PatchTST, and iTrans- former) improved by over 3%p, suggesting that their methodologies can be reconsidered for TSC.
5.2 MODEL ABLATION
Table 1: Model ablation for four hypotheses (H1-H4). The best and the second-best are highlighted in bold and underline, respectively.
(HI) kernel size modular SSM skip connection
(H2)
(H3)
(H4)
Model
aggregation adaptive pool X: the others
avg.
avg.
avg. # win # lose rank rank /draw /draw test
0.02Z d: modular TV/II
/: not use
acc
K:
x: TV only
x: use
MambaSL
/
78.779
79.802
2.43
w/o H1
77,413
80.215
2.53
25
22
0.217
w/o H2
75.659 77.075
5.93
30
5
0.000
w/o H3
77.715 79.163
3.50
22
14
0.071
w/o H4
X (fully conn)
76.438 77.722
4.87
24
10
0.003
X (avg pool)
76,900 78.610
3.60
22
15
0.044
(max pool)
76.448 78.535
3.90
12
0.004
(last step)
75.856 76.848
4.87
0.001
only H2
X
X (avg pool)
77.00 77.092 77.935
20
13
0.011
Mamba
X
X
X (fully conn)
73.286 74.243
26
0.000
Table D summarizes the average classification accuracy of ablated models for each hypothesis (HI- H4), providing a systematic exploration of MambaSL's design space. The full results are included in appendix C.2 MambaSL remained competitive with state-of-the-ar baselines, even when excluding HI, H3, or H4 individually. Also, none of the three ablations resulted in statistically significant gains in the rank test. Motivated by this, we additionally ran an ablation that only applies H2; how- ever, this variant failed to surpass the second-best baseline and showed statistically inferior perfor- mance (p- 0.011). 1)1, suggesting that all our four hypotheses contribute positively performance.
Disabling H1 (A: 3) yielded the highest average accuracy, surpassing 80%. Its overall ranking, however, remained second, as the gain was largely driven by marginal accuracy changes in the AF and SMJ datasets, which both have only 15 test samples. Additionally, on three datasets-AF, ER, and -eMB-where MambaSL was outside the top 10 (appendix C.I.. a fully connected layer outperformed other classifiers. This suggests that for fixed-length datasets, fully connected readouts can be competitive as long as overfitting is controlled. Nevertheless, across the full benchmark, our multi-head adaptive pooling consistently performs better with greater generalizability.
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5.3 FURTHER ANALYSIS
Table 2 shows the classification accuracy of MambaSI. under eight configurations of time variance hyperparameters, 03. 8, and 0c.
Time variance ablation
Table 2: Ablation time variance of A, B, and C. The best and the second-best are highlighted in bold and underline, respectively.
No single combination is overwhelmingly superior, yet models with the LTI setting (all marked X) tend to outperform the fully TV setting (all marked of selective SSM. This contrasts with the findings of [u & Dao (2024) in their ablation study, where the fully TV setting was superior for lan- guage modeling.
TV-A
TV-B
TV-C
avg. acc (10)
avg. acc
avg. rank
X
X
77.280
77.400
3.87
X
/
76.938
77.102
3,93
X
/
X
76.451
77.042
3,80
V
V
76.422
77.767
3.60
X
X
76.253
77.707
3,63
X
J
76.299
77.148
3.70
V
X
76,309
77.085
3.80
75.938
77.055
4.13
8.55
T9-6EX
1V-3:X TV-0:/ Ti-ai Te-0:/
Te-3i TW-6:x Te-t7 TV-6:x TV-I
10-8:X
19-17
19-817 TV-8:X TV-B:X
10-857 TV-B:/
TV-B:X 19-8:X re-B3 Tw-8-x TW-B:/ Te-B17 TV-B1/ 1V-8:7
TV-CiX TH-C TV-EV TV-CIX TV-CiX THEE TV-C1
TV-C1/ TV-CIX TV-CIv TV-CI TV-E TV-CIX TV-OiX TV-EIV
configuration
confiuuratio
Figure 7: Visualization of classification accuracy MambaSI. along TI/TV configurations on (left) EC and (right) HW datasets, in order of maximum accuracy.
In some datasets, the trend is particularly clear. As shown on the left side of Figure 7 in EC, accuracy increases as more parameters are set be time-invariant, with A having the largest impact. This indicates that dynamic warping of the sampling intervals can distort the chemical reaction signal of ethanol, which can impair classification performance. Furthermore, similar the right of Figure using TI-B consistently yields better results than using TV-B across many datasets (HW, HB, CT. DDG, UW, and RS), while the opposite trend is also observed (P EMS, CR, PD, and PS). By contrast, no dataset exhibits consistent preference with respect to C. These observations support our hypothesis that while the time variance A and can be dataset speaset- that C has limited impact, as output-channel mixing naturally occurs in the classifier. The visualizations for all 30 datasets provided in appendix C.4
Class label: Bedmintonn_ Clear
Class label: Soatminton_Smmane
class
10
Badminton_Emasn
Badminton, Clear
Squash Forehandfloast
0.08
-20
Squash BackhandRoast
0.04
Figure 8: Visualization of adaptive pooling on the RS dataset for two samples. Line plots show per- timestep logits 4, and heatmaps illustrate the corresponding per-timestep weights at-
Analysis of pooling behavior To further examine the role of adaptive pooling, analyzed the RS dataset, which contains relatively few classes and yields strong performance under max pooling. The left plot in Figure 8 depicts sample where the predicted label differs between simple aver- age of per- per-teeeeeeeee timestep logits and the weighted average using a;- As shown, large logit values do not necessarily correspond large weights, and regions with high weights are often localized. As in the right plot of Figure there are also samples with nearly uniform weights, effectively reducing adap- tive pooling to average pooling. This adaptive weighting enables MambaSI. to outperform average pooling, achieving the Sacond-highest accuracy on the RS dataset (appendices) C.1 and
Effeet of model depth Although our main architecture uses a single Mamba block as shown in Figure 2 also ablated the number of stacked blocks examine whether deeper MambaSI.

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Published as a conference paper at ICLR 2026
further improves performance. Table compares variants with 2, and 3 Mamba blocks under the same training protocol (full results are provided in appendix C.5). We observed that a single layer achieved the best average accuracy and the best average rank across the 30 UEA datasets. While performance gradually decreases as the number of layers increases, the differences are small, and all three variants perform similarly overall. Given these results, we conclude that a carefully configured single-layer Mamba is sufficient as backbone for TSC in this regime, and we adopt the l-layer MambaSL as our default architecture for both simplicity and efficiency.
Table 3: Classification accuracy (%) of MambaSL according to its model depth. The best and the second-bes are highlighted in bold and underline, respectively.
depth
avg. acc (10)
avg. acc
avg. rank (10)
avg. rank
win/draw of lose/draw
rank test
1
78.827
79.819
1.30
1.33
2
78.853
79.316
1.90
1.80
26
15
0.035 0.004
3
78.918
79.279
2.10
2.07
24
12
5.4
ADDITIONAL EXPERIMENTS
UEA benchmark classification considering data leakage issue To account for the data-Icakage
issue reported for TSLib in TSC settings (Akacik & Hoogendoorn 2025 Talukder et al. 2024). we
additionally trained MambaSL under the Inception Time protocol (Ismail Fawaz et al. (2220). where the checkpoint with the lowest training loss used for evaluation. For comparison, we selected the top three high-performing models from each source-HC2 (Midllehurst et al. (02T). BORF (Spin- nato et al. 2024), and our own experiments to form a comparable set classical baselines. These results, along with the corresponding evaluation tables, provided in appendix C.6 While Mam- baSI. shows lower accuracy under the stricter Inception'Tine protocol than under the default TSL b setting, it remains competitive relative to the strongest non-DL baselines reported in previous studies and in our re-evaluation.
Classification on recent domain-specific dataset Although the UEA archive (Bagnall et al. 2018) covers a broad range of TSC datasets, it does not include more recently released real-world benchmarks. Therefore, we additionally evaluated MambaSI. on ADFTD (Miltiadous et al.) 2023) and FLAAP (Kumar & Suresh, 2022). two of the most recent datasets benchmarked by Med- former WWag et al., 2024a) in the medical and human activity recognition (HAR) domains, re- spectively. Classification results compared with strong baselines in Medformer are provided in ap- pendix C.7 Across both datasets, MambaSL achieves competitive performance without any domain- specific modifications, demonstrating its generalizability to recent TSC tasks.
CONCLUSION
In this study, we introduce four hypotheses to why Mamba has remained underexplored in TSC despite its success in other sequence domains. With minimal architectural modifications, our single- layer Mamba framework, MambaSI., achieved state-of -ott---------tttt- performance across the entire UEA benchmark. By re-examining 20 prior baselines, we further highlighted systematic differences across backbone structures and demonstrated that Mamba can serve as strong multivariate TSC backbone.
Nevertheless, our work has two main limitations. First, while our model is robust across the UEA benchmark, it rarely achieves overwhelming superiority on individual datasets, implying that practi- cal use may require domain-speciff adaptations. For instance, prior knowledge of input embedding may constrain kernel size, making receptive-field scaling with sequence length suboptimal in cer- tain domains. Second, our study is limited to the first version of Mamba (Gu & Dao 2024). While Mamba-2 (Dao Gu 2024) introduces an efficient parallel Mamba block, applying time-variance modularization requires low-level optimizations beyond the scope of this study.
Overall, our findings demonstrate that a carefully configured single-layer Mamba can serve as a competitive backbone for TSC. Beyond TSC, future work may further improve Mamba by leverag- ing receptive-fiel field scaling and time-variacee control, along with shallow, task-aligned architectural choices and adaptive aggregation strategies.
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REPRODUCIBILITY STATEMENT
Source code and scripts are publicly available at GitHub repository and datasets, along with the
best-performing model checkpoints, have been uploaded to Google Drive
The source code offers a complete implementation of Mamba and all other baselines, with the exception of dataset and checkpoint downloads. The repository includes grid search script gen- eration, local training execution, pretrained model evaluation, and result visualization.
Full training logs, corresponding to the grid search results reported in Figure 4 and Table I are also provided in the repository. We are confident that the experimental setup and hyperparam- eter settings mentioned in appendices A and B are identical to the scripts and logs inside the repository.
The dataset folder also contains preprocessed CWT and ROCKET features for TSCMamba, compatible with the released checkpoints.
Detailed reproduction instructions are available in the README, md file.
THE USE OF LARGE LANGUAGE MODELS
The authors acknowledge the of ChatGPT-5 and Google Translate only during the writing pro- cess to ensure rigorous and concise academic English writing.
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A EXPERIMENTAL SETUP
Environment All experiments were implemented in Python 3.12.8 and PyTorch Most Most were on NVIDIA GTX 1080 Ti (IIGB), while a few required NVIDIA A (40GB) on Google Colab due to memory limits. Classical baselines used the aeon toolkit (Middlehurst et al. 2024). and all deep models except TSLANet were integrated into the Time-Series Library (TSLib) (Wu et al., [023] Wang et al.] 2024b). For models supported in TSLib, we used the provided code; for others, we adapted the authors' official implementations. All source code is available our GitHub repository (see reproducibility statement).
Dataset The UEA archive (Bagnall et al. 2018 provides 30 multivariate TSC datasets with di- verse sample sizes, input dimensions, lengths, and class counts (Table As TSLib has become widely used framework, a subset of 10 datasets (EC, FD, HW, HB, JV, PEMS, SRS1, SRS2, SAD, and UW) is commonly used in recent TSC benchmarking practices.
Table 4: Summary of the 30 UEA datasets used in experiments
Dataset
Code
Train Size
Test Size
Length
Variables
Classes
EthanolConcentration
1751
FaceDetection
FD
5890
3524
62
144
2
Handwriting
HW
150
850
152
26
Heartheat
HR
204
205
405
61
2
Vowels
270
370
7-29
12
9.
PEMS-SF
PEMS
267
173
144
963
7
SellRegulationSCPI
SRS1
896
SellRegulationSCP2
SRS2
200
180
1152
2
SpokenArabicDiggtts
SAD
6599
2199
4-93
13
10
UWaveGesturel Librar
UW
120
320
315
8
Articulary WordRecognitiin
AWR
275
300
144
25
AtrialFibrillation
15
640
3
BasicMotiios
100
CharacterTrajectories
CT
1422
1436
60-182
20
Cricket
CR
108
72
1197
12
DuckDucGGeese
DDG
50
50
270
1345
S
DigenWorms
128
131
17984
Epilepsy
137
138
206
ERing
270
6
FingerMovements
FM
316
100
50
28
2
HandMaavemennDineccioo
HMD
160
74
400
10
4
iinghett
TW
25000
25000
2-22
200
10
Libras
LIB
180
180
45
15
LSST
2459
2466
MotorImagery
100
3000
64
NATOPS
NATO
180
180
24
6
PenDigits
PD
7494
3498
8
10
PhonemeSpectra
PS
3315
3353
217
11
39
RacketSports
151
152
30
6
4
Stand Walkuup
SWJ
2500
3
Metries
The evaluation metrics reported in our figures and tables are summarized as follows:
avg. ace: Average accuracy (%) across all UEA datasets.
avg. ace (10): Average accuracy (%) for 10 UEA datasets, following prior TSC practices.
avg. rank: Average rank of each model across all UEA datasets.
# of top-N: Count. of datasets in which each model is in top-N.
# of win/draw: Count of datasets where proposed model achieved higher or the same accuracy.
# of loss/draw: Count of datasets where proposed model achieved lower or the same accuracy. rank test: p-value of Wilcoxon signed-rank test (Wilcoxon, 1945). A value smaller than the significance level (0.05) indicates that proposed model ranked greater than the selected.
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B HYPERPARAMETER SETTINGS
B. BASIC HYPERPARAMETER SETTINGS
We standardized experimental hyperparameter settings across all models focus model tuning, where most were chosen as common values across prior UEA benchmarks and TSLib:
Batch size: 16
(some experiments used smaller batch size as described in appendix C.1
Learning rate: 0.001
Learning rate scheduler: none
Optimizer: RAdam (Liu et al. 2020)
Train epochs: 100
Patience: 10
Dropout: 0.1
Seed: 2021 (for DL models)
Although this unified setting may deviate from model-specific defaults, extensive model-level grid searches compensated for potential performance degradation (see appendix C.3
B.2 MODEL HYPERPARAMETEE SETTINGS FOR GRID SEARCH
We primarily considered the hyperparameter settings that were tested in either the original papers or the source code scripts, and also referred the hyperparameters that were set in TSLib scripts. For TSF-origin and foundation models that do not provide classification scripts, we tend to choose the same smaller values than in forecasting scripts since empirically found that many UEA datasets require compact model sizes compared to TSF datasets. Over 200 candidate settings were evaluated per model-dataset pair, except for some models that did require significant extension. The hyperparameters explored are as below:
Non-DL
-DTW D: fixed implementations (no learnable hyperparameters).
ROCKET, HC2, Hydra, MR+Hydra: 3 combinations
seed: 1,2
Other parameters are fixed the default optimal setting of the original papers since the models themselves have either large number (Ie-4) of kernels or ensemble mechanism.
MLP-based models
DLinear: 15 combinations
moving_avgr 0.5%, 1%, 2%, 3%, 4%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50% of sequence length
Since DLinear only has one model parameter to be tuned, we didn't 't over-expand the number of hyperparameter combinations beyond 200.
LightTS: 100 combinations
model: : 32, 64, 128, 256, 512
chunk.size iz:: 1/21 1/2 of sequence length
Since LightTS only has two model parameters to be tuned, we didn't over-expand the number of hyperparameter combinations beyond 200.
MTS-Mixer: 256 combinations
e.layers: 2
d model: 128, 256, 512, 1024
d.ff: 0, 2, 4, 8, 16, 32, 64, 128
facuC: 0 (False) if d_ff is 0, else 1 (True)
down down_sampling www.dwiwwwwwwwwww.tmomooo...m... 0, 1%, 2%, 3%, 5%, 7.5%, 10%, 12.5% of sequence length fac (False) if down samplimg ing window else 1 (True)
use_norm: (True)
16

---

Published as a conference paper at ICLR 2026
CNN-based models
TimesNet: 252 combinations
e_layers: 2, 3, (d_model, d_ff): (8,16), (8,32), (8,64), (16,16), (16,32), (16,64), (32,32), (32,64), (32,128), (64,64), (64,128), (64,256), (128,128) (128,256)
top k: 1,
num.kernels: 4, 6
ModemTCN: 252 combinations
ffn ratio: 2,
patch.size: 2.5%, 5%, 7.5%, 10%, 15%, 20%, 25% of sequence length
patch.stridd: 50% of patch.size
(numublocks, large_size, small.size, dims): ("I", "I3", "5", "32"), ("1", "13", "5", "64"), "13", "5", "128"), ("1", "13", "5", "256"), (*1 1", "9 9", 55 "32 (","""9" "55", "64 "64 "",""""" "122 "13 13", "5 "32 (""), ("11" "13 13" "55", "64 128"), 13", "5""" "128 256"), 1" 1",", 5555" "32 64 128"), ("I 1", "13 13 13", "5,,, 128")
TSLANet: 252 combinations depth: 2, emb_dim: 32, 64, 128, 256
mlp ratio: 1,
patch.size: 2.5%, 5%, 7.5%, 10%, 15%, 20%, 25% of sequence length patch.stride: 50% of patch_size
masking_ratio: 0.4
IC3: 1 (True)
AS3: (Truc)
adaptive.filter: (True)
load_frrom.pretrainedd (True)
pretrain.1r:
pretrainneepcch:55 50
21
down sampling method: "conv"
down.samplingg_ayeers: 1, 2,
down.sammmllig...indd:::: num.kernels:6
e Layers: 1, 2, 3, 4
d.model: 16, 32, 64
d_ff: 16, 32, 64
n.heads: 8
top.k: 2, 3
17

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Published as a conference paper at ICLR 2026
Transformer-based models
FEDformer: 250 combinations
movingLavg: 0.5%, 1%, 2%, 3%, 5%, 10%, 20%, 30%, 40%, 50% of sequence length
e_layers: 2
d model: 32, 64, 128, 256, 512 d.ff: 128, 256, 512, 1024, 2048
n_heads: 8
ETSformer: 240 combinations
e.layers: 1,2
d_model: 32, 64, 128, 256, 512
d_ff: 64, 128, 256, 512, 1024, 2048
n heads: 8
top.k: 1,234,4
Crossformer: 288 combinations
e layers: 1, 3
d.model: 32, 64, 128, 256
d.fÉ: 64, 128, 256, 512
n.heads: 4
factor: 3, 10
seg.len: 6, 12, 24 (change the code of TSLib to modify the fixed value)
PatchTST: 252 combinations
e.layers: 1, 3,
d.model: 16, 32, 64, 128
d.ff: 64, 128, 256
n_heads: 4 if 1_model E (16, 32) else 16
patch s ze: 2.5%, 5%, 7.5%, 10%, 15%, 20%, 25% of sequence length patch.stride: 50% of patch_size
GPT4TS: 252 combinations
e.layers: 3, 5,
d.model: 768 (fixed since the model use pretrained GPT-2)
d.ff: (fixed since the model use pretrained GPT-2) patch.sized 1/25- 1/5 of sequence length
patch.strides 25%, 50%, 100% of satch.size
iTransformer: 240 combinations
e_layers: 3,
d_model: 64, 128, 256, 512, 1024, 2048 d.ff: 64, 128, 256, 512, 1024
n.heads: 8
factor: 1,3
18

---

Published as a conference paper at ICLR 2026
Shape-based model
InterpGN: 243 combinations
dnn_type: "FCN"
num_shaplet: 5, 10, 15
lambda div: 0, 0.1, 1.0
lambda.reg: 0, 0.1, 1.0
epsilon: 0.5, 1.0,
gating.value: 0.5, 0.75, 1.0
distance func: "euclidean"
memoryeefficiet:: False
sbm_cls: "linear"
Mamba-based models
TSCMamba: 288 combinations
d model: 64, 128, 256
e.layers: 1
expand: 2
d.conv:
d. (d 32, 64, 128
no.rocket: 0 (False)
0 0 (False)) 1 (True)
addittive.fusion: 0 (multiplicative fusion), (additive fusion)
(ax
(max pooling)
channelltoken..ixinngg (False)
only.foowaard.scar:: 0 (False)
2 flip)
flverse.fipp: 0 (False)
p
patch.stride:
rescale.size:
variation: 64
initial.focus: 1.0
MambaSL (ours): 240 combinations
d-model (dm)): 32, 64, 128, 256, 512, 1024
d.ff (d,): 1, 2, 4. 8, 16
tv.dt (0x): 0 (False), I (True)
tv.B (0)): 0 (False), (True)
tv.c (#c:): 0 (False), 1 (True)
use.D: 0 (False)
expand: ]
d.conv:
19

---

Published as a conference paper at ICLR 2026
C ADDITIONAL EXPERIMENTAL RESULTS
C.1 FULL RESULTS OF TSC MODELS ON THE UEA BENCHMARK
Table presents the full experimental results corresponding to Figure As mentioned in ap- pendix A some experiments could not be run on the GTX 1080 Ti GPU with batch size 16. Foot- notes in the table indicate such experiments. We only used the A100 GPU for cases that could not be run on the 1080 Ti with batch size 4. Also, note that only one hyperparameter combination was tested in our limited resource environment for TSCMamba (Ahamed & Cheng 2025) pair, since preprocessing took over 2 days and training took over 12 hours per epoch.
Table 5: Classification accuracy (%) ) of TSC models on 30 datasets from the UEA archives. The best and the second- best are highlighted in bold and underline, respectively.
Classification results from DTWp ModemnTCN.
Don-DL
MLP-based
CNN-based
MR+ Hydra 22023
MTS-
Modem
DTW o
1994
ROCKET
2020
HC2 2021
Hydra 2025
DLincar 2023
LighITS 2022
Mixer
2023
TimesNet 2023
2024
TCN
EC
32.319
44.487
52.852
54.373
60.076
31.179
32.319
30.798
33.460
32.319
$2.866
61.606
61.379
60.982
61.294
69.495
68.473
68.530
70.148
66,998
HW
60.706
59.294
56.000
56.353
53.529
23.765
25.059
19.647
37.529
30.118
HB
71.707
75.610
78.537
76.585
77.073
76.585
77.561
79.024
83,902
78.049
JV
95.946
83.784
98.378
97.838
98.378
96.486
96.757
95.946
98.649
98.649
PEMS
71.098
84.393
97.688
98.266
82,659
82,659
87.861
82.081
87.861
86, 86,705
SRSI
77.474
85.666
89.078
86.689
94,881
92.833
92.833
78.840
95.150
93.515
SRS2
53.889
55.556
54.444
58.889
55,000
57.222
59.444
58.333
60.000
60.000
SAD
97.226
99.227
99.136
99.136
99,045
96,953
98.226
99.454
99,545
99.500
UW
90.313
94.063
94.063
94.375
94.063
82.813
89.063
83.
88.438
88.438
AWR
98.667
99.333
99.333
99.333
99.333
97.667
98.333
99.000
98.661
98.667
20.000
6.667
20.000
26.667
6.667
53,333
73.333
60.000
66.667
73.333
BM
97.500
100.000
100.000
100,000
100.000
87,500
100.000
100,000
100.000
100,000
98.816
99.304
99.304
99.164
99.373
98.120
98.955
97.981
98.747
99.304
CR
100.000
100,000
100.000
100,000
98.611
91.667
93.056
97.222
97.222
97.222
DDG
58,000
52,000
70.000
68.000
60.000
66.000
56.000
68.000
68.000
66.000*
EW
61.832
91. 91.603
97.710
98.473
98.473
42.748
$4,198
66.412
63.359
64.122
EP
96.377
99. 99.275
100.000
100,000
100,000
65.217
97.101
66.667
96.377
96.377
ER
91.481
98. 889
98.889
98 98.889
99.259
91.852
94.444
77.778
95.556
97.407
FM
$3.000
57.000
61,000
55,000
60.000
62.000
64.000
63.000
66.000
67.000
HMD
18.919
51.351
48.649
51.351
36.486
66.216
66.216
70.270
72.973
66.216
TW
48.692
38.524
66.796
67.052
65.744
18.996
71.676
72.424
64.024
75.588
LIB
87.222
90.556
94,444
93.333
93,333
70.556
89.444
34.444
88.333
90.000
LSST
$5.109
64.031
66.383
66,504
64.274
34.023
41.525
38.
12.
43.593
50.000
54.000
50.000
$4.000
51.000
64.000
68.000
66.000
68.000
67.000
NATO
88.333
89.444
91.111
90,000
93,333
96.111
97.222
95.000
98.333
95.556
97.713
98.228
97.656
97.856
97,284
92.481
96.998
46.941
98,914
98.513
PS
15.121
27.826
33.015
33.164
33.642
6.651
16.224
5.428
14.524
13.749
RS
80.263
90.789
90.132
91.447
90.132
78.947
81,579
86,842
89.474
84,211
SWI
20.000
46.667
40.000
40.000
66.667
53,333
73.333
66.667
66.667
80.000
avg. (10)
70.354
74.368
78.156
78.349
77.600
70,999
72.760
69.578
15.168
73.429
avg.
68.020
73.306
76.866
77.124
76.320
68.247
75.308
69.001
76.921
76.938
avg. rank
16.03
11.20
8.90
8.83
9.40
16.80
11.83
13.90
8.67
9.13
of top-1
1
3
4
7
8
0
3
of top-5
2
10
13
13
12
6
9
of top-10
5
15
19
17
16
5
11
17
16
of win/draw
29
24
20
20
21
28
25
28
24
24
of lose/draw
2
9
14
13
11
3
7
8
rank test
0.000
0.001
0.056
0.034
0.015
0.000
0.000
0.000
0.004
0.001
All experiments were run 1080 Ti with butch size 16 except below:
The experiments were with batch size 8
The experiments were run with batch size 4
The experiments were run on A000 GPU
20

---

Published as a conference paper at ICLR 2026
Table 5: (Continued.) Classification accuracy (%) of TSC models on 30 datasets from the UEA
archives. The best and the second-bes, are highlighted in bold and underline, respectively.
(b) Classification results from TSLANet to MambaSL. "*" indicates "former" Transformer-bseed models.
shape- based
CNN-based
Transformer- based
Mamba-based
TSLANet 2024
Time Mixer++
FED*
ETS
Cross
PatckTST
GPT4TS
Transt 2024
InterpGN 2025
TSC Mamba
MambaSL
2023
2022
2023
2023
2023
(proposed)
2025a
2025
EC
31.939
34.601
33.840
33.080
43.346
32.700
32.319
31.939
28.897
30.798
42.586
FD
66.969
69.665
69.892
68.331
69.637
68.076
68.615
68.417
63.791
70.204
69.296
HW
62.000
33.647
33,412
35.647
30.353
29.529
34.118
31.294
58.706
45.059
60.824
II
79.512
80.488
78.049
78.537
79.024
73.659
79.024
78.049
80.488
78.537
80,488
IV
98.919
99.189
97.568
98.378
98.378
97.027
98.108
98.108
99.730
98.378
98.649
PEMS
89.017
89.017
89.017
87.861
89.595
89.017
88.439
91.329
86.127
90.173
85.549
SRSI
89.761
92.150
75.085
92.150
93.857
89.078
93.174
93.857
90.444
92.833
92,491
SRS2
63.889
60.556
59.444
60.000
61.667
61.667
60.556
58.889
58.333
62.222
65.000
SAD
98.045
99.545
99.454
99.000
98.863
98.818
99.591
99.318
99.864
95.816
99.955
UW
94.375
90.625
79.688
89.688
88.438
89.375
89.063
90.000
88.438
95.313
93.438
AWR
99.333
99.000
92.333
98.667
98.667
98.667
99.000
99.333
99.333
AF
66.667
66.667
73.333
80,000
73.333
80,000
66.667
66.667
46.667
66.667
53.333
BM
100.000
100.000
95.000
100,000
100,000
85,000
100,000
97.500
100,000
100,000
100.000
CT
99.164
99.373
97.284
98.468
98.955
98.398
99.373
98.886
99.791
99.513
99.721
CR
91.667
98 61
90.278
98.611
97.222
97.222
97.222
97.222
100,000
100,000
100,000
DDG
74.000
74.000
42.000
42,000
72.000
40.000
66.000
60.000
76,000
50.000
70.000
EW
81 81.679
61.069^
63.359
70.992
58.015
57.252
66.412
59.542
82.443A
88.550
83.969
EP
100.000
94.928
92.754
92.754
95.652
98.551
89.855
80.435
97.826
99.275
97.826
ER
85.926
96,296
95.926
96.296
97.407
96.667
96.296
94,815
87.778
97.037
93.704
FM
68.000
67 670
72.000
66.000
64.000
63.000
66.000
64.000
67.000
66.000
71.000
HMD
48.649
72.973
71.622
63.514
66.216
63.514
70.270
58.
45,946
60.811
70,270
IW
10.012
64.192
65.244
64.868
73.712
60.936
61,396
73.436
67.180
64."
66.304
LIB
95.556
88.333
93.333
90.000
90.556
81.667
87.778
91.667
97.222
91.667
91.667
LSST
51.014
44.444
40.998
41.241
41.768
56.732
38.078
40.673
$9.570
60.057
45.580
MI
71.000
67 07.000
65.000
67.000*
68.000*
69.000
67.000
67.000
67.000^
67.000
69.000
NATO
99.444
96.111
96.111
93.333
93.889
80,556
96.111
88.889
98.889
92.222
98,889
PD
99.028
98.599
98.599
98.542
98.513
98.485
98.513
98.428
99.342
98.513
99,257
PS
4.921
15.508
11.691
14.316
14.256
15.091
13.123
11.542
33.075
23.800
30.331
RS
94.079
91.447
84.868
85.526
87.500
85.526
86.842
81,579
91.447
90.789
92.763
SWJ
73.333
73.333
66.667
66.667
80.000
86,667
73.333
73.333
60.000
73.333
73,333
avg. acc (10)
77.443
74, 948
71.545
74.267
75.316
72.895
74.301
74. 120
75.482
75.933
78.827
acc
76.263
77 17.
74.128
75.716
77.427
74.729
76.065
74.797
77.700
78.405
79.819
avg. rank
7.80
7.03
12.33 1
10.63
8.50
12.60
9.50
11.83
7.87
7.10
5.30
# of top-1
7
2
1
1
5
5
top-5
12
6
14
18
top-10
21
13
10
20
25
27
if of win/draw
18
23
23
27
21
24
26
25
21
20
lose/draw
15
10
14
rank
0.029
0.005
0.000
0.000
0.005
0.000
0.000
0.000
0.026
0.039
All experiments were run on 1080 with batch size 16 except below:
The experiments were run with batch size
The experiments
with batch size
experiments were
GPU
Litle hyperparameter search was performed since took over two weeks conducting a single experiment.
21

---

Published as a conference paper at ICLR 2026
C.2 FULL RESULTS OF MODEL ABLATION ON THE UEA BENCHMARK
Table presents the complete ablation results corresponding to Table I For clear comparison, we also performed the hyperparameter grid search for each ablated model. Model rankings were com- puted within the ablated variants, while additional experimental results are included for reference.
Since removing H2 reduces the number of valid hyperparameter configurations to one-eighth, we extended the search space of Mamba's s hyperparameters (expand and d.conv) and compared the results. As expected, performance improved with broader exploration, yet the model still underper- formed on datasets such as EC and NATO, where the LTI setting remains advantageous.
Table 6: Classification accuracy (%) of ablated models on all 30 datasets from the UEA archives. d_model, d_state, expand, and d_conv are four basic hyperparameters that affect the model capacity of Mamba. The best and the second-best are highlighted in bold and underline, respectively.
ablation
additional
w/o H1
w/o H2
w/o H3
w/o H4
proposed
Mamba
w/ H2
w/o H2
kernel size
V: k 0.026
=
modular SSM
V: moddula TV/II
X
V
J
X
V
X
TV only
skip connection
X
x use D
aggregation
X
V: aduptive pool.
the others
(full)
(avg)
(max)
(last)
(full)
(avg)
d_model
32..1024
3024
32.. 1024
32..
1024
3024
32..1024
322.1022
32.4
322.1024
1024
dsttate
1.166
1.166
1.,16
1,,16
1..16
1.,16
1,,16
1.116
1.
1.116
1.116
expand
1
1,2,4
d_corv
4
1,2,4
# exp
240
30
240
240
240
240
240
240
30
240
270
BC
33.080
32.319
39.544
34.601
32.700
41 825
33.080
42.586
30.418
34,221
33,460
FD
69.296
68.587
69.211
70.006
70.034
69 69.012
66.827
69,296
68.785
69.154
69,495
HW
60.235
54.000
58,000
47.294
59.882
45 529
56.706
60.824
30.941
60.471
56.000
HB
80,976
79.024
80.000
80.000
80, 80,488
80,976
78.537
80.488
77.073
80.976
80.976
IV
1896449
98.108
98. 689644
98.649
98, 108
98.919
98.919
98.649
97.297
97.838
98.919
PEMS
85.549
82,659
87,283
90,751
82,659
85 85.549
89,595
85.549
93.064
83.815
85,549
SRSI
92.150
89.761
91.809
93.174
91, 468
90.102
87.713
92,491
92.833
91.126
91.809
SRS2
61.111
61.111
61.111
600.000
63.333
61.111
65.000
58.889
62.222
SAD
99.955
99.773
99,909
99.727
99.909
99 864
99.818
99.955
99, 181
99.955
99.818
UW
93.125
91.250
92.188
89.063
93,750
89.375
86.250
93.438
84.375
92.813
93.438
AWR
99.333
99.333
99.000
99.000
99.333
99.000
97.000
99_333
97, 667
99.000
99.333
AF
73.333
46.667
53,333
66
$3.333
66 667
66.667
53.333
66.667
46 667
60.000
BM
100,000
100.000
100,000
100,000
100.040
100.000
100.000
100.000
97,500
100.000
100,000
CT
99.582
99.443
99.513
99.095
99.791
99.234
99.582
99.721
98.816
99.791
99,582
CR
100.000
100.000
100.000
98.611
100.000
100,000
100.000
100.000
97. 97.222
100.000
100.000
DDG
70.000
66.000
64.000
72.000
80.000
70.000
$2,000
72.000
74.000
EW
85,496
83.206
84,733
67.176
83,969
84,733
51.908
83.969
67.176
75.573
85.496
EP
97.826
97.826
97. 101
96.377
97,826
98.551
97.826
97. 97.826
92.754
97.101
97.826
ER
93.704
91 .852
94.444
95.926
185
91.852
92.22/
93.704
94.444
96.296
94.815
71,000
64.000
65.000
69,000
000
0000
08.000
71,000
64.000
68.000
HMD
72.973
64.865
68.919
68.919
68.919
56.757
50.000
70.270
52.703
74.324
68.919
IW
66.304
65 65.952
62,.20
65.124
666.044
66.392
66.584
66.304
62.684
66.396
66.756
LIB
91.667
91.667
90,556
86.111
91.111
88 889
88. 88.889
91.667
82.778
92.778
92.222
LSST
45.580
43.593
46.513
43.390
45.174
43.593
42.701
45,580
37.307
43.917
44,282
MI
65.000
65 .000
70,000
70.000
65,000
66.000
65.000
69.000
65.000
63.000
66.000
NATO
98.889
97.222
98,889
97.778
98.889
98.889
98.889
98,889
95.556
98.333
98.333
PD
99.257
99.142
99.371
99.114
99.428
99.085
98.999
99.257
98.799
99.314
99.314
PS
29.615
29.764
30.063
16.880
31.792
27.528
27.140
30.331
13.809
31.226
29.764
RS
92.763
90.132
93.421
90,789
90,789
94.737
88.816
92.763
84,211
94,079
90.789
SWJ
80.000
60.000
66.667
73.333
fi6.667
66.667
666.667
73.333
73.333
$3.333
73.333
acc (10)
77.413
75.659
77.715
76,438
76.900
76.448
75.856
78.827
73.286
77.092
77.169
avg. acc
80.215
77.075
79. 19.163
77.722
78.610
78.535
76.848
79.819
74.243
77.935
79.348
avg. rank
2.53
5.93
3.53
3.60
3.90
4.87
2.40
winktraw
30
22
24
22
25
26
20
M of lose/draw
22
14
15
12
9
13
17
rank test
0.217
0.000
0.071
0.003
0.044
0.004
0.001
0.000
0.011
0.106
22

---

Published as a conference paper at ICLR 2026
C.3 COMPARISON WITH PREVIOUSLY REPORTED RESULTS
Although we compared various hyperparameter settings against the original paper to ensure as fair comparison possible, our results may not represent those in the original paper due to some differences in basic hyperparameter settings (e.g. fixing the learning rate to 0.001). Therefore, we compared our results with previously reported results, and the results are presented in Table]] 7
Among the baseline models, only papers that directly evaluated the classification results are used and mentioned in the table. If multiple papers reported results for a particular model-dataset pair, the highest value was used for comparison. For Hydra and MR+Hydra, the results reported in BORF (Spinnato et al. 2024) were used. Note that we approximated the reported values using the number of test samples since they were typically rounded to one or two decimal places.
The metrics used in T are as follows:
report I ours: Count of datasets on which our experimental results of grid search achieved higher or equal accuracy compared to the reported results. A value higher than "report 2 ours" indicates that existing reports likely underestimated the model.
report 2 ours: Count of datasets on which our experimental results of grid search achieved lower or equal accuracy compared to the reported results. A value higher than "report S ours" indicates that our experiments likely underestimated the model.
Average difference between our experimental accuracy and the reported ac- curacy, calculated only for datasets with paired results. A positive number indicates that our experiments achieved higher aceuracy, while a negative number indicates the opposite.
acc difference:
Table 7: Comparison of our experimental results and previous reported results.
(a) Comparison results on TSC-specific models.
Distance
TpC-specific
DTW 1994
ROCKET (2020
HC2 2021
Hydra 2023
MR-Hyyda 2023
InterpCNN 2025
TSCMamba 2025
TimnaNat TSLAND TSCMamsb
TimesNet
ours
ours
original
OUTS
BORF
ours
BORF
ours
original
ours
original
ours
BC
32.281
32.319
45.209
44,487
79.087
52. $2.852
54.791
54.373
58.897
60.076
30.418
28.897
62.015
30.798
52,900
52.866
64.699
61.6 60.66666
71.348
61.379
301
60.982
61.901
61.294
66.402
63.791
69.401
70.204
HW
28.600
60.706
58,800
59. 49.294
56.341
56.000
46.600
56.353
51.400
53.529
61.882
58.706
$3.306
45.059
HB
71.707
71.707
75.610
75.610
72.878
78.537
76.098
76.585
73,220
77.073
79.512
80.488
76.585
78.537
JV
94.892
95.946
98.189
83.784
98.378
97.811
97.838
97.811
98.378
99.189
99.730
97.000
98.378
PEMS
71.098
71.098
75.087
84,393
89.
97. 97.688
80.289
98.266
77.514
82.659
88.439
86.127
90.173
90.173
SRSI
77.713
77.474
90.785
85 666
87. $7.884
89.078
86.689
86.689
95.188
94.881
92.491
90.444
92.491
92,833
SRS2
53.889
53.889
54, 444
55, 556
50.444
54.444
58. 889
58.889
56.722
55.000
57.778
58.333
66.722
62.222
SAD
96.298
97.226
99,200
99.227
98.899
99.300
99.045
99.818
99.864
99.000
95.816
UW
90.313
90.313
94.406
94.063
94.875
94.063
90.906
94.375
94.094
94.063
91.875
88.438
93.813
95.313
98.667
99.333
99.567
99.333
99.333
99.333
99.667
99.000
97.000
99.333
AF
20.000
20.000
6.667
28 000
20.000
13.333
26.667
6.667
6.667
33,333
46.667
67.333
66.667
BM
97.500
100.000
100.000
99.000
100.000
100.000
100.000
100.000
100.000
100.000
100.000
100.000
100.000
CT
98.816
98.753
99.304
99.304
98 600
99.164
99.199
99.373
99.791
99.791
98,997
99.513
CR
100.000
98.611
100.000
100. 100.000
100.000
98.611
100.000
98.611
98.611
100.000
100.000
98.611 100.000
DDG
58,000
$2.000
$2,000
800
70.000
68.000
60.000
64.000
60.000
68.000
76.000
62.000
50.000
EW
61 832
22.626
91 603
89, 466
97.7 70
98.
98.
183
98.473
83,969
82, 443
87.023
88,550
EP
96.377
98.551
99.275
99.783
100,000
100.000000000
100.000
100.000
100.000
99.275
97.826
97.101
99.275
ER
91.481
94,074
98. 889
519
98, 889
519
98, 889
98, 889
99, 2.5
95, 185
87.778
91.519
97.037
FM
$3.000
61.000
$7.000
55.200
61. 100
.00
55 500
56.000
60.000
64.000
67.000
69.000
66.000
HMD
18.919
50,000
51.351
39.730
48. 649
622
51.351
36.486
36.486
48.649
45,946
71.622
60.811
IW
48.692
10.000
38.524
66. 76.796.00
67. 900
67.052
66.700
65.744
66.752
680
14,300
68.244
LIB
87.222
90,556
66.
94.444
93.278
93.333
92.778
93.333
97.222
97.222
90.000
91.667
LSST
55.109
54, 100
64.0
63. 694
66. 36.383
60.702
66.504
63.601
64.274
60.665
59,570
60.300
60.057
MI
50.000
54.000
$3,200
50.000
50.000
54.000
48.000
51,000
64.000
67.000
62.000
67.000
NATO
88.353
83.333
89 .44
89.222
91.111
87.222
90.000
91.722
93.333
98.889
98.889
94,444
92.222
97.713
97.341
228
99.557
97.656
96.801
97.856
96.801
97.284
94.142
99.342
98.542
98.513
PS
15.121
17.599
27.826
49.
33.015
32.401
33, 164
35,401
33,642
380
33,075
24.659
23,800
RS
80.263
86.184
90.789
93.026
90.1 902
91.382
91. 91.447
90.789
90.132
90.789
91.447
91.447
90.789
SWJ
20.000
46.667
46.667
44.000
40.000
40.000
40.000
60.000
66.667
53,333
60.000
73.333
73.333
report < ours
8
23
15
28
21
18
17
report 2 aurs
ac difforence
3.385
2.056
0.224
3.320
0.747
0.278
0.080
23

---

Published as a conference paper at ICLR 2026
Table 7: (Continued.) Comparison of our experimental results and previous reported results.
(b) Comparison results on foundation models.
Foundation
TemesNet 2023
MC 2024
TSLANet 2024
TimeMixer++ 2025a
GPT4TS 2023
original
TSC Marini
TST AN
original
revisit
OuLES
orgiinal
13CMurbe
DALES
original
oars
osiginal
TSI ANoe TXCMonis
OUIF
EC
35.703
27.719
13,460
36.312
31.901
32.319
30.418
31.939
39.886
34.601
34,18%
25.475
32.319
68.601
67.469
70.800
68.700
66.998
66.771
66,969
71.799
69.665
69. 20.2000
65.579
68.615
HW
32. 106
26. 176
37.529
30,600
28.400
30.118
57,882
62.000
26.506
33.647
32.706
3.765
34.118
HB
78.000
74,488
83.902
77.220
77.122
78.049
77.561
79.512
79.122
80.458
77.230
36.585
79.024
IV
98.405
97.8.18
98. 649
98.811
108
98 649
99,
98.919
97.892
99.195 18.
08.599
108
98. 108
PEMS
89.595
88. 150
87. 861
89.075
83.179
86.705
83.8115
89.017
90.983
89.017
87.919
$7.283
$8.439
SRSI
91,809
77,440
92. 150
93.413
92.799
93.515
91,809
89,761
13.106
208
91.45
93, 174
SRS2
57.222
52.833
60.000
60.278
61.722
60.000
61.667
63,889
65.611
60.556
59,389
51.667
60.556
SAD
99,000
98.358
99.545
98 699
98.099
99,500
99.909
98.045
99 800
99.545
99. 99,200
99 09.363
99,591
UW
85.313
83.125
88.438
91.250
94.375
88.188
90.625
063
AWR
96.167
98. 667
98.667
99.000
99.333
99.000
93.33 13.
98, 667
AF
33.333
66.667
73.333
40,000
66.667
66.667
33.333
66, 667
BM
100.000
100.000
100,000
100.000
100.000
100.000
92,500 100.000
CT
97.981
98. 747
99, 99,304
98.747
99.164
99.373
98.259
99.373
87,500
97.22
97.222
98.611
91.667
98.611
8,333
97.222
DDG
$6,000
68 000
66.000
24,000
74,000
74,000
50,000
66.000
EW
58.779
63.335
64.122
41.221
81,679
61.069
48.092
66.412
18.
96.377
96.377
98.551
100.000
94.928
85,507
89.855
ER
94.074
95.556
97.407
91.481
85,926
96.296
95.926
96.296
67.000
61.000
$7,000
66.000
HMD
50.000
72.973
66.216
52,703
48.649
72.973
18.919
70.270
IW
10.0010
64.024
75.588
10.000
10.012
64.192
10.000
61.396
LIB
77,833
90,000
92.778
95.556
88.333
LSST
59,2019
44.120
43,593
66.338
51.014
44.444
46.391
38.078
51,000
68.000
67.000
62,000
71,000
67.000
50,000
67.000
NATO
81.833
98. 98.333
95.556
95.556
99.444
96.111
91.667
96. 96.111
PD
190
98.914
98.513
98.939
99.028
98.599
97.742
98
14.524
13.749
17.751
4,921
3.012
13.123
RS SWI
82,632
89.474
84.21
90.
94.079
91.447
76.974
86.842
53.333
66.667
80,000
46.667
73.333
73.333
33.333
73.333
report S our
8
27
8
19
6
29
report 2 ours
2
6
2
4
2
acc difference
1.593
9.014
(0.761)
0.835
2,161
21,330
(0.341)
0.330
15.617
(c) Comparison results on ToF-origin models.
TSF-onigin FEDrormer 2023
DLinear 2023
LighTTS 20273
MTS MTS-Misee Mixer
ETStarmer 2022
Crossformer 2013
PT 2023
iTrnnformer 2024
2N12
1SL ANe
onrs
TimesNo
ours
MalerTCN
ours
Timesher
ORIS
TreeNNa
ours
TSI ANA TSCMacha
ours
TELANs TSCMaaba
ours
TineMibrees ours
TSChlanat
BC
464
379
29.696
32.319
33,802
798
31.217
33,840 33.840
28.099
(S0)
97.9
43.346
32,814
32.700
28, 28.099
31 91.999
FD
67.999
69.495
67.500
68.473
70.199
68.530
65.999
69.892
66.300
68. 133
68.700
69.637
68.961
68.076
66.300
45.417
HW
27.000
23.765
26.106
35,059
26.000
19.647
28.00 28.000
33.112
32.506
64.
800
30.353
29.600
29.529
24,200
31.294
76.585
75.122
77.561
77.122
79.024
73.
76.049
71.220
76.585
75.610
IV
97.838
96,486
96. 189
75.
94.297
95. 946
9% 411
97.568
95. 892
9A. 378
99.110
98. 378
98.649
97.027
96.995
98. 108
PEMS
82.081
82.659
88.361
87.861
80.925
82.081
90.925
89.017
86.012
87.861
85, 65.896
$9.595
89.306
89.017
87.919
91.329
SRSI
795
92.833
91,200
92.
92.491
SR82
57.222
51.111
59.444
55,000
58.
389
49.444
55.00
5.0000
60.000
58.278
667
57.778
667
54, 389 58.50
SAD
96. 690
96, 953
100.000
98 226
97.399
99.454
000
99, 454 100.000
99:000
97. 899
9% 863
99.682
98 98.818
95 998
99 318
UW
82.094 82.813
30.313
89.063
82,313
83.125
85.313
79.688
85.000
89.688
85.313
S5.438
85.813
$9.375
85, $5,906 90.000
AWR
97.667
99.000
92.333
97.667
98.667
AF
66.667 53.133
73.333
60.000
73.133
80.000
46.667
73.333
53.333
80.000
66.667
BM
85,000 87.500
100.000
100.000
95.000
100.000
90,000
100.000
92,500
85.000
97.5 770
CT
34. 98.120
95.955
97.981
97.284
468
98.329
98.955
97.347
98. 398
886
CR
91 667 91.667
93.056
47. 272
90.278
98. 611
84,722
97 27.
84,722
97.222
222
DEG
62.000 66.0001
56,000
68.000
42,000
40.000
44.00 (000
72,000
24,000
401000
60.000
BW
221 42,748
54.198
66.412
63.359
70.992
54,96 $4,962
58.015
$4,198
57.252
39.542
EP
65.217
97.101
66.667
92.754
73.188
65.942
98.551
ER
91.481 91.852
91.144
77.778
95,926
96.296
94.074
97.107
92.593
96.667
91.315
FM
64.000 62.000
64.000
63.000
72,000
66.000
64,000
64.000
62.000
63.000
$4.000
HMD
58. 66.216
66.216
70.270
71.622
63,514
58.108
66.216
58.108
63.514
58.108
IW
10.000 18.996
71.676
72,424
65.244
64
10,000
73.712
10,000
LIB
73.355 70.356
89.444
34 244
93.353
90,000
76.111
90.556
81.111 81.667
91 667
E ESST
35.770
34.0233
41,525
185
40.99%
41. 14.
42,818
41 41.768
67.79%
56.732
40.673
MIL
65.000
68.
61,000
NATO
96.111
97.222
95,0000
96.111
93. 33.
88. 333
93 889
96
80.556
PD
92.939
92.188
96.998
46.941
98.599
98.542
93.651
98.513
99.231
98.485
98.428
PS
7.101
6.651
16.224
5.428
11.691
14.316
7.551
14.256
11.691
15,091
11.542
RS
78.94710
78.947
81,579
26.842
84,868
85.
81,579
87.500
84,211
81.579
SWJ
50,000
$3.333
73,333
66.667
66.667
66.667
53,333
80.000
60.000
86.667
73.333
report curs
21
28
18
10
report ours
Dec difference
1.237
2,338
(1.298)
0.879
3,305
9.010
5.262
3.598
24

---

Published as a conference paper at ICLR 2026
C.4 VISUALIZATION OF TIME VARIANCE ABLATION
Figure D shows visualizations of MambaSL's classification accuracy results on the UEA benchmark, tested for various hyperparameter combinations along with TI/TV configurations. Among 240 com- binations, 10 sets of hyperparameter combinations that showed high performance regardless of the TI/TV configurations were selected (8x10=80 total) and depicted in box and line plots, Each line represents the same hyperparameter settings except for time variance configurations.
average scorrev
0.4
5.55
3.46
Te-as
TW-3ix
TV-d=z
Ty-der TV-az
Te-3:/
TV-áia Ty-der TV-dor TV-Bt/
TV-0ax
Te-Bt> TW-Bt> TV-0:x
TV-81x TV-B:r TV-B2r
TV-Bex TV-B:7
TV-Bt/
TV-Box TV-B:r TV-Box TV-Blx
TV-Cir
TV-Cs
TV-Cix TV-Cxx
TV-C1 TV-Cix TVHC
TV-Co
TV-Eix TV-Div TV-C TV-Ci TV-C TV-Cox
configuration
configuration
(a) FthanolConneentration (RC)
(h) FaceDetection (FD)
everge sccurs
0.4
0.5
Te-tir
Th-ter
Deddea TW-tex TV-dza
TV-Azz TV-tex
Th-tzx Teattr TW-te Wedex TW-ti Ti-lex TW-azy Ty-Die Te-Biz TV-Bix TV-B:x TV-B TV-B:> TV-B TV-B TV-tte TeCii TV-ttr Te-t TV-C1 TV-Ci TV-C> TV-t
Te-0:> TV-D2x Te-B
TV-Bix TV-B:> TV-B:/ TV-B:z TV-B2>
TW-C TeC TV-Cix TV-Di TV- TV-Cia TW-C1
configuation
mofigraation
(c) Handwriting (HW)
(d) Heartbeat (HB)
Sverag accursc
0.07
6.8
TV-a
TV-dee TV-824
Te-a
TV-diz
TV-4
TW-i Tidder TV-air
TV-Aax 19-Be7 Te-Be7
Te-air
TV-A:z
TV-
TV-a
Ti-der
TV-á:r TV-4
Te-tor
Te-Bea
T9-8t7
TV-Baa TV-B57 TV-Ber T0-Bor
TV-Bt7
TV-Bta
TV-B5X TV-B:x TV-Bor TV-Rex
TV-Cir
TV-Cox
TV-Cor
TV-C:1 TV-C5v TV-E1z TV-t Te-Cor
TV-GoA TV-C:r
TV-Co/
TV-C:x TV-Ci7
TV-fox Te-Cor TV-C57
configumation
unfiiguration
(c) Japanese Vowels (JV)
(i) PEMS- SF (PEMS)
0.52
3.05
0.9
0.:00
0.00
Te-a1>
TV-A
TV-3ix
TV-BIX TV-BEY
TV-3ix
TV-8:a
TV-Biz
TV-B
TY-BEK
TV-Ct>
TV-Cix TV-C
TW-Ci
TV-C
TV-E
TV-C
configuraion
confguration
(g) SellRegulationSCPI (SRS1)
(h) SellRegulationSCPP2 (SRS2)
avergge eccurscy
0.993
TW-diz TV-diz TV-ii/ TW-ii
TW-ii TV-tiz TV-dix TV-dix
TV-4ix Te-a1 TW-ii
TV-61/ TV-a
TV-dis TV-áiz Ti-div
TW-B1> TV-Bix TV-Bi> TV-Bix TV-B17 TV-Bi> TV-Bix TV-Bix
TV-Bix TV-Bix TV-Bix
TV-B1/
TV-Bix TV-Bt/ TV-Bi> TV-Bi/
TW-Ci TW-C1 TW-Cix TV-Cix TV-C12 TV-C1 TV-CI7 TV-Cix
Ty-E2x TV-C1 TV-C TV-Cix TV-Cix TV-Cix TV-C1/ TV-C2/
confguration
confguration
(i) SpokenArabicDigits (SAD)
(j) UWaveGesturrLLibbray (UW)
Figure 9: Visualization of classification accuracy of MambaSL along TI/TV configurations on the UEA benchmark, in order of maximum accuracy.
25

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Published as a conference paper at ICLR 2026
ovenge S0curSt
0.03
0.005
TW-3e
TW-3:
TW-3i
TW-3r
TW-31/
TW-5e TW-5i
TW-5r TV-3ar TW-di Ti-a:> Ti-6 T8-6ix TV-dar
TW-dr
TV-t
TW- TW-0i/
TW-8:/
TV-81x
TV-Bix
TW-Bt TW-3i
TV-Bix Te-Bor Te-ti> Te-1:>
TK-Bee TW-B:a TV-8ix TW-0:/
TW-Ci
TW-C1
TW-Ci TV-C1/ TV-Ciz TV-C1 TW-Ci TW-CI
TV-Ci TV-CI TV-C1 TV-Cix TV-Cix TV-C22 TV-E:> TV-CI/
cnniguunttoo
(k) AnticularyWordRecogniitn (AWR)
(l) AtrialFibrillation (AF)
areEgg accurocy
TV-der TW-dea TV-daa
TV-tox To-tey TW-aty Te-aey TV-der
TV-Aoy TV-dca Te-tea Te-tor Te-dor
TW-ter Te-ler TV-Bor TV-Bor TV-Bor TV-Bty TV-CiA TV-Esr Th-tix TV-t:> TV-Cix TV-tis TV-tsr Te-tsr
TW-Acx
TV-Bar TV-Bea TV-Rov
TV-Brr TV-8ix TV-aiy TV-Bey TV-Be/
TV-Bea TV-Rca TV-Rtx TV-B:x
TV-Cox TV-Csr TV-Cxx TV-Es> TV-Cix TV-Ci/ TV-Cix TV-Co
configrration
configuration
(m) BasicMotions (BM)
(n) CharacterTrajectoriis (CT)
0.65
6.6
0.35
TV-dix
TV-aiz
TV-dex Te-ai
TV-AzA
TV-Biv TV-Bik TV-Bix TV-B:X TV-Bix
TV-B:A TV-Bix
TV-Bix
TV-Bi7
TV-Co>
TV-Dax
TV-Ca
TV-Cor TV-Eox TV-Co>
TV-Eor TV-Cir
TV-tor
TV-Cox
TV-
TV-Exx
TV-Cay
TV-tax
TV-tir TV-tor
configunation
unfiguration
(0) Cricket (CR)
(p) DuckDuckGeese (DDG)
0.85
0.98
average Scorarcy
0.46
0.79
0.94
0.7
0.02
Te-1 TV-416
TV-a TV-11> TV-11a
TV-iie Th-ii TV-ai
6.9
TV-A
TV-A Te-daa TW-1 Ti-dix TV-11 TV-A
TV-02 TV-B:/
T--: TV-31/ TV-Div TV-Bir TV-Bix Te-Biz
TV-BiA TV-B:/ TV-Bi
TV-Bta TV-0ix TV-Ber TV-11 TV-D:/
TV-Cix TV-Cix TV-Ctr TV-C1v TV-Civ TV-Ciz TV-C TV-Cir
TV-Ctv TV-Civ TV-cix TV-C TV-Cix TV-Cox TV-Cir TV-C
configuraion
configuration
(q) EigenWorms (BW)
(r) Epilepsy (EP)
overge 903ra0
TV-3ix TV-Biz TV-Bi> TV-Biz
TV-11 TV- TV-ii TV-BiA
TV-C1 TH-C TV-CI
TV-C TV-C1
TV-IA
THCC TV-01A TH-
TV-tx
contiguration
confguraton
(s) ERing (ER)
(1) FingerMovements (FM)
Figure 9: (Continued.) Visualization of classification accuracy of MambaSL. along TI/TV configu- rations the 30 UEA datasets, in order of maximum accuracy.
26

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Published as a conference paper at ICLR 2026
0.6
2.44
0.33
TW-d:
TW-der
TW-6:x
TV-5:7
TW-diz
Thaar
Té-as Te-ti Te-l:r
Ty-di TW-d
TW-5e TV-622
TV-3ax
Ty-bor
TW-dz TW-da
Te-Bta TV-0ea
TV-Bez 1V-31/
TV-di>
Ty-Bre TV-iia TV-B:> TV-0:a TV-827 19-02a Te-ti TW-0t/
TV-C1
TV-Cxx
TW-C=> TV-Cix TV-C1/
TV- TV-Ci
Ty-EI TVHCi
TV- TV-C:> TV-Cix TV-Eix TV-Cix TV-E
anfiguration
toniiurrttio
(u) HandMooveennDirrccton (HMD)
(v) InsectWingbeat (IW)
0.92
0.46
0.41
0.000
6.4
0.38
Te-dzr
T9-As Te-d: Te-ty Te-dex Te-bo
Ty-tay
TV-der
TV-t2/
TW-ty Tw-tex TV-R:x TV-Bor TV-Rtr TV-B TV-R
TV-dza Tu-tey
TV-Bor
TV-Box TV-Ber
TV-3i/ TV-Bor TV-8i7 TV-Bex TV-B:X
TV-Be TV-B:y
TV-8:
TV-Co TV-Eix TV-Cx TV-Ci TV-Ci TV-t Ty-Csx TV-CV/
Ty-Es TV-Cix
TV-E TV-t:> TV-C1/ TV-Cix TV-Ci TV-ti
configuration
confguration
(w) Libras (LIB)
(x) LSST (LSST)
grengg acounac
3.00
0.6
0.98
0.04
9.92
TV-Aiz TV-dix TV-aiz
TV-dex TV-aiz TV-B:r TV-Bix
TV-AzA TV-azr
TV-Aix
TV-ai/
TV-3ix TV-Bt> TV-Bix
TV-B:v TV-Bix
TV-Biv
TV-Cor
TV-Cax
TV-CaM
TV-C:x TV-Eix
Te-Co>
TV-Eor TV-Cir
TV-Ezx TV-Cox
TV-Co/
TV-Ear TV-Cax TV-Cax TV-C1/ TV-tor
configunation
unfiguration
(y) Motorlmagery (MI)
(z) NATOPS (NATO)
avergee Scourcy
0.99
0.29
0.28
0:27
0.28
TW-a Te-ai TW-a TW- TV-a TV-3
TW-3ix TV-
TV-Ai
TV-dir
TV-A TV-62 TV-41/ TV-d2r TV-117 TV-4ix
TV-Biz TV-Bi TV-Bi TV-Biz TV-Bix TV-Bi TV-Bix TV-Bi
TV-Bt> TV-B:/
TV-Bi TV-Be> TV-0ix TV-Bix TV-01 TV-Dix
TV-ciz Te-Civ Te-civ TV-Ciz TV-Ciz TV-Cix TV-Civ TV-Cii
TV-Ctv TV-Cir TV-Cix TV-C TV-Ci7 TV-Cox TV-C1r TV-Coa
configurstio
configuration
(aa) PenDigits (PD)
(ab) PhonemeSpectra (PS)
evergg scorary
wwweage aaroomaay
0.9
62.8
TV-3ix TV-B TV-Biz
TV-Biz
TV-Biz
TV-BiA
TV-Biz
TV-8i2
TV-BI TV-8iz
TV-C1A
TV-01 TV-Cxx
TV-01>
TV-GA
TW-E
TV-01
THCM TV-IA TV-Gi
contiguration
configuration
(ac) RacketSports (RS)
(ad) StandWalkJump (SWJ)
Figure 9: (Continued.) Visualization of classification accuracy of MambaSL. along TI/TV configu- rations on the 30 UEA datasets, in order of maximum accuracy.
27

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Published as a conference paper at ICLR 2026
C.5 FULL RESULTS OF MAMBASL PERFORMANCE ACCORDING TO MODEL DEPTH
Table presents the complete comparison results corresponding to Table 3 Although using three layers yielded the highest average accuracy on the 10 datasets commonly evaluated in TSLib, this observation does not hold when extended to the full set of 30 UEA datasets. When examining the av- erage rank on these 10 datasets, we find that the single-layer configuration consistently outperforms the multi-layer variants, indicating that the higher average accuracy for deeper models is driven by a small subset of datasets. In particular, the HW dataset exhibits nearly 5%p improvement with 2-3 layers compared to single layer, which disproportionately affects the average accuracy metric.
Table 8: Classification accuracy (%) of MambaSL according to its model depth 30 datasets from UEA archives. The best and the second-best are highlighted in bold and underline, respectively.
model depth
(proposed)
2
3
EC
42.586
41.065
41.825
FD
69.296
68.785
69.098
HW
60.824
65.765
66.353
НB
80.488
80.488
80.488
IV
98.649
98.649
98.378
PEMS
85.549
85.549
86.705
SRSI
92.491
92.150
91.126
SRS2
65.000
62.778
62.222
SAD
99.955
99.864
99.864
UW
93.438
93.438
93.125
AWR
99.333
99.333
99.333
AF
53.333
53.333
60.000
BM
100.000
100,000
100.000
CT
99.721
99.652
99.721
CR
100.000
100.000
100.000
DDG
70.000
70,000
68.000
EW
83.969
83.206
85,496
EP
97.826
97.826
97.101
ER
93.704
95.185
94.074
FM
71.000
67.000
65.000
HMD
70.270
68.919
68.919
IW
66.304
64.772
65.596
LIB
91.667
91.111
89.444
LSST
45.580
44.039
42.539
MI
69.000
67.000
66.000
NATO
98.889
98.889
98.333
PD
99.257
99.171
99.200
PS
30.331
31.435
30.987
RS
92.763
93,421
92.763
SWJ
73.333
66.667
66.667
avg. acc (10)
78.827
78,853
78.918
avg. acc
79.819
79.316
79,279
avg. rank (10)
1.30
1.90
2.10
avg. rank
1.33
1.80
2.07
# of win/draw
26
24
# of lose/draw
15
12
rank test
0.035
0.004
28

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Published as a conference paper at ICLR 2026
C.6 CLASSIFICATION RESULTS ON THE UEA BENCHMARK UNDER INCEPTIONTING TIME SETTING
To address the data-Icakage concern raised for TSLib, we trained MambaSL under the Inception- Time protocol, which selects the checkpoint with the lowest training loss (Ismail Fawaz al. 2020). For comparison, we selected the top three high- perfhreeforming models from each source- HCr Mid- alehurst et al. 2.2T). BORF (Spinnato et al. 2024), and our own experiments sentative conventional TSC baseline set. For HC2, we used only the results from fold 0, the official
form a repre-
train-test splif] and for BORF, we approximated the reported accuracies using the number of test
samples as described in appendix C.3
Table 9 summarizes the results. Although MambaSI. achieved slightly lower average accuracy than HC2 and Hydra, it remained competitive, achieving the largest number of top-1 results (15 datasets) and outperforming every non-DL baseline in direct head-to-head comparisons. These findings indi- cate that MambaSL maintains strong performance under leakage-free model selection.
Since all experimental hyperparameters and optimization settings were fixed to the same values as in our original experiments, the performance differences between Tables 5 and 9 arise solely from the change in the model selection criterion.
Table 9: UEA classification accuracy (%) of MambaSL using the Inception protocol (selecting the checkpoint with the lowest training loss). The comparison set includes the top three models from each HC2 (Middlchurst et al. 202T), BORF (Spinnato al. (024), and our evaluations. The best and the second-best are highlighted in bold and underline, respectively.
HC2 reported
2021
BORF reported (2024)
our experiments
CIF
DrCIF
HC2
Mini Rocket
D:CIF
MR+
HC2 2021
Hydra 2023
MR+ Hydra
MambaSL (proposed)
Hydra
2020
2021
2021
2021
2021
2023
2023
EC
73.384
69.202
77.186
48.289
63.118
58.935
52.852
54.373
60.076
39.165
FD
62.713
62.003
66.033
58.201
59.109
61.890
61.379
60.982
61.294
68.331
HW
35.647
34.588
54.824
51.765
36.235
51.412
56.000
56.353
53.529
59.294
НB
78.049
79.024
73.171
74.146
79.024
73.171
78.537
76.585
77.073
76.585
JV
99.189
97.297
97.838
98.378
97.838
98.378
98.649
PEMS
100,000
100,000
100.000
82.659
100.000
77.457
97.688
98.266
82.659
83.237
SRSI
86.007
87.713
89.078
91.809
87.031
95.222
89.078
86.689
94,881
88 737
SRS2
50,000
49.444
50.000
54.444
51.111
56.667
54.444
58.889
55.000
61.111
SAD
99.318
97.817
99.318
99.136
99.136
99.045
99.727
UW
92.500
90.938
92.813
93.125
93.750
94.063
94.063
94.375
94.063
92,500
AWR
98.333
98.000
99.333
99,333
99.333
99.333
99,333
99.333
99.333
99.333
AF
33.333
33.333
26.667
13.333
40.000
6.667
20.000
26.667
6.667
46,667
BM
100.000
100.000
100.000
100,000
100.000
100.000
1 100,000
100.000
100,000
100,000
CT
99.304
98.
99.234
99.304
99.164
99.373
99,582
CR
98.611
98.611
100.000
98.611
98.611
98.611
100.000
100.000
98 08.611
100,000
DDG
44.000
54.000
56.000
70,000
48.000
64.000
70.000
68.000
60.000
70.000
EW
91.603
92.366
94.656
93.893
93.130
183
97.710
98.473
98,473
83.206
EP
98.551
97.826
100.000
100,000
100.000
100.000
100,000
100.000
100,000
97.826
ER
98.148
99.259
98. 889
97.778
99.259
98. 889
98.889
98.889
99,259
92.593
FM
52,000
60.000
53.000
57.000
47,000
56.000
61,000
55.000
60.000
64,009
HMD
59.459
$2.703
47.297
39.189
$1.351
36.486
48.649
51.351
36.486
62.162
IW
67.300
66.400
66.700
66.796
67.052
65.744
64.524
LIB
91.111
89 444
93.333
91.667
89 444
92.778
94,444
93.333
93,333
90,556
LSST
57.259
55.596
64.274
65.085
56.488
63.585
66.383
66.504
64,274
25.182
MI
50.000
44.000
$4.000
50.000
$0.000
48.000
50.000
$4.000
$1,000
62.000
NATO
85.556
84.444
89.444
94,444
82.778
91.667
91.111
90.000
93.333
98.889
PD
96.741
97.656
97.913
97.313
96.798
97,656
97.856
97.284
99,228
PS
26.543
30.778
29.049
29.407
39.994
35.401
33.015
33.164
33.642
29.824
RS
88.158
90.132
90,789
85.526
88.816
90.789
90.
91.447
90.
90.789
SWJ
40.000
53,333
46.667
40.000
40.000
60.000
40,000
40.000
66.667
60.000
of win/draw
18
19
17
19
20
20
18
19
18
of lose/draw
10
9
13
13
12
14
16
15
14
rank test
0.081
0.088
0.3
0.046
0.072
0.082
0.307
0.240
0,221
acc difference
2.058
1.416
123)
2.061
1.777
1.218
(0.076)
(0.334)
0.470
"The full set of reported results is available timeseriesclasifcctiio.cmm
29

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Published as a conference paper at ICLR 2026
The higher performance observed in re-evaluation of certain non-DL baselines in Table stems from running three random seeds and reporting the best-performing run. Using only a single seed (seed 0) yields results comparable to those originally reported for HC2 and BORF.
To further assess stability, we conducted two additional trials for MambaSL and report the mean and standard deviation across three trials in Table TO
In contrast to Table 9 where HC2 and Hydra showed the strongest performance based on the best- performing trial, averaging across all trials indicates that MR+Hydra achieves the strongest mean performance and exhibits more consistent results. Nevertheless, MambaSL. remains competitive, outperforming all conventional non-DL baselines in direct head-to-head comparisons and achieving the largest number of top-1 results (13 datasets). The Wilcoxon signed-rank test indicates no sig- nificant difference between MambaSL and Hydry, suggesting comparable overall effectiveness under this protocol.
Table 10: Mean and standard deviation of UEA classification accuracy (%) of MambaSL. using the Inception Time protocol (selecting the checkpoint with the lowest training loss). The comparison set includes the four non-DL models from our own evaluations. The best and the second-best are highlighted in bold and underline, respectively.
our experiments
ROCKET
HC2
Hydra 2023
MR+Hydra (2023
MambaSI. (proposed)
[2020)
(002
EC
99.333 =0,000
99.111
10.388
99.222
10.192
99.222
19.1
99.11
20.385
FD
6.667
17.778
#3.849
17.778
4:7698
6.667
1:0000
42.222
#7.698
HW
100.000 00,00
100,000
1:0000
100.000
1:0000
100.000
10.000
100.000
100000
HB
99,257 =0,40
98.793
#00464
99.071 1:0.006
99.327
+0,040
99.513
1:0000
JV
100.000 0.000
99.537
99.537 +0.802
98.61
10.000
100,000
PEMS
48.667 33.055
59.333
=336114
50,000 19.0779
56.667
#3.055
65.333
1:6429
SRS1
90.331 = 1.166
94,402 =:4.473
93.639 +:1341
97.201
+ 1.16
82.188
#: 1.166
SRS2
98.792 =0418
100.000
100.000
100.000
97.343
SAD
98.642 00,22
98.395
#06566
98.765 10.214
98.395 10.932
91.728 114497
UW
42.966 1.742
50.824 13,189
50. 4:3.27
58. 175 1:6662
37. 37.896 1.335
AWR
61.076 =0.512
55.789
+48842
57.454 1:4565
60.036
1.12
17.263 +0,928
AF
55.000 22,000
55.333
16.028
53.667 4:2.00
59.333 1.155
58.667 16.800
BM
49.550
36,486
38.739 153366
34.234
#22064
63.514
#2341
CT
58,863 00378
49.451 15.831
53.098 155335
52.588
10.941
58.667 1.007
CR
74,472 =1.228
76,260 222200
75.772 +1.08
75.610
11.29
72.683 43.683
DDG
38.348
64.677 #3.635
61.279 +5.004
65.692
1:0074
64.264
#02227
EW
82.883 -0.780
94.324
17.022
90.270 16.622
97.928
10.563
98. 198 10.780
EP
90,185 =0.321
93.148 1.398
92.222 +0.962
93.333 =0.000
89,815 # 1.288
ER
63.869 -0227
62.693
64.355
63.328
18. 194
FM
51,667 =2.022
47.667 #4.041
52,000 +:2000
50.333 20,577
60.667
::4163
HMD
88.333 =1.111
89.074 1.951
88.333 1..470
91 1.881
+: 6.60
97.778
:: 1.470
IW
83,815 =0.578
85,356
=009227
91.908 10.022
80.539 +2.030
82.659 #3.218
LIB
98.132 =0.100
96,884 #:0.22
97.484 10.619
97.170 10.103
99.123 #02259
LSST
27.378 =0517
31,663 1.89
31.057 11.827
32.995 10.707
28.7 28.780 1: 1.000
MI
90.789 -.,00
87,939
+2.310
90.789 10.658
89.035
1.005
89,912 11519
NATO
85.324 -0341
87.827 1.00
86.121 4..710
94.084
1.0359
87.372 42,365
PD
52.407 =3.349
52.407 1.776
54.630 +4.170
53,889
#0.922
56.667
#4.194
99.151 -01311
98.530
10.533
98,909 10.355
98.939
10.095
99.576
10.13
RS
46,667
=0,000
37.778
#3,849
40.000
+0.000
57.778
155.3%
53,333
111.447
SWJ
93.646
00477
92.
1.740
93.021
93.750
10.541
91.979
# of win/draw
18
18
18
16
# of lose/draw
14
14
13
rank test
0.127
0.169
0.185
0.461
acc difference
2.608
1.360
1.171
(0.063)
30

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Published as a conference paper at ICLR 2026
C.7 CLASSIFICATION RESULTS ON ADFTD AND FLAAP
While the UEA archive provides a standardized benchmark for multivariate TSC, it mainly con- sists of datasets released between 1999 and 2018, many of which relatively clean and small in scale (Bagnall et al., 2018). To evaluate the robustness of our model beyond these settings, we additionally tested MambaSL on two more recent and domain-speeiff TSC datasets.
Following the evaluation protocol of Medformer (Wang et al., 2024a), we selected ADFTD Miltii adous et al. 2023) from the medical domain and FLAAP (Kumar & Suresh 2022) from the human activity recognition (HAR) domain. A summary of both datasets is provided in Table 11
Table 11: Summary of ADFTD and FLAAP datasets
Dataset
Domain
Samples
Length
Variables
Classes
File Size
ADFTD
2023
EEG
69752
256
3
19
2.52GB
FLAAP
2022
HAR
13123
100
10
60.2MB
Preprocessing and experimental settings were aligned with the official Medformer implementation. We first evaluated MambaSL using the 240 hyperparameter configurations employed for the UEA experiments, after which we conducted four additional trials some best-performing configura- tions, resulting in a total of five runs. Table 12 reports the average accuracy and F1-score across these trials. For comparison, we also include the results of the strongly performing baselines re- ported by Medformer (Wang et al.) 2024a): Crossformer (Zhang & Yan 2023), Reformer (Kitaev) 2017), TCN (Bai al. 2018), ModemnTCN (Luo & Wang 2024 and Mamba (Gu & Dao 2024). Since the original Medformer paper did not report results for TCN, ModemTCN, and Mamba on ADFTD, these entries are left blank.
at.l. 2020), Transformer /Vaswani
MambaSI performed competitively against previous baselines both datasets, and achieved the highest accuracy on FLAAP, particular, we observe more than 10%p improvement over the vanilla Mamba. Taken together, the results on ADFTD and FLAAP show that MambaSL remains stable across domains and dataset scales, reinforcing that its performance is not limited to the characteristics of the UEA archive.
As shown in Table 12
Table 12: Classification accuracy and F1-score (%) of MambaSI. and other baselines on ADFTD and FLAAP datasets. The best and the second-best are highlighted in bold and underline, respectively,
ADFTD
FLAAP
accuracy
FI-score
accuracy
Fl Fl-score
Crossformer (2023)
50.45
45.50 11.70
75.84
10.52
75.52 10.66
Reformer (022))
50.78 11.17
47.94 11.59
71.65
11127
71.14 11.45
Transformer (207
50.47 +2.14
48.09 +1.59
74.96
11.2
74.49 +1.39
TCN 2018
66.48 1.66
65.29 +1.74
ModemICN (2024)
74.80 10.96
74.35 10.5
Mamba (2024)
64.87 +2.78
64.14 42.70
Medformer (2024a)
53.27 +1.54
50.65 11.5
76.44 10.64
76.25 10.65
MambaSI. (proposed)
51.68 10.89
48.73 13305
77.47
1.033
77.59 11.15
31


Reviews:
[{"review_id": "pog7d1pEPA", "reviewer": "Reviewer_wsQb", "summary": "This paper introduces MambaSL, a framework that minimally modifies the selective state space models and projection layers of a single-layer Mamba architecture for time series classification. Experimental evaluations conducted on 30 datasets from the UEA benchmark demonstrate that MambaSL consistently outperforms 20 competitive baseline methods.", "questions": "1. In univariate time series classification, the original **InceptionTime** paper evaluates models based on the checkpoint with the lowest training loss, and the same evaluation strategy is adopted in **[2]**. Under a comparable setting, how does **MambaSL** perform relative to **HC2** and **MultiRocket+Hydra** on the 30 UEA multivariate time series datasets?  \n\n2. On the 128 UCR univariate time series datasets, following the **InceptionTime evaluation protocol**—where the model with the lowest training loss is used for testing—how does **MambaSL** compare in classification performance with **HC2** and **MultiRocket+Hydra**?  \n\n3. Beyond the evaluation setups in **InceptionTime** and **[2]**, some studies (e.g., **TSLANet**) adopt a different experimental protocol that uses **20% of the training set as a validation subset** for model selection on both UCR and UEA datasets. Under this widely used setting, how does **MambaSL** perform compared with **HC2**, **MultiRocket+Hydra**, and other competitive baselines?  \n\n4. Compared to 20 baseline methods, what are the advantages and disadvantages of the proposed **MambaSL** method in terms of runtime?\n\n**Reference**\n\n[2] *Inherently Interpretable Time Series Classification via Multiple Instance Learning*, ICLR, 2024.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. This paper applies the Mamba architecture to time series classification tasks and achieves superior performance compared to Transformer, MLP, and CNN models on multivariate time series classification benchmarks. This demonstrates the strong potential of Mamba for time series classification modeling.  \n2. The authors provide open-source code for the proposed model, along with implementations of the baseline methods, ensuring the reproducibility and transparency of the experimental results.", "weaknesses": "1. The paper’s title, introduction, and model design focus primarily on time series classification without providing an in-depth discussion of how variable relationships are modeled in multivariate time series. In this case, the experiments are limited to the 30 datasets from the UEA multivariate time series classification archive, while excluding the 128 univariate datasets. This omission weakens the paper’s motivation and makes it difficult to assess whether the proposed method achieves state-of-the-art performance under univariate settings.  \n\n2. Although the application of Mamba to time series classification is commendable, the paper does not clearly explain how the proposed MambaSL framework captures intrinsic temporal properties—such as temporal dependencies, inter-variable relationships, and short- or long-term sequence characteristics. The introduction and contribution sections also fail to clarify how MambaSL learns discriminative feature patterns beneficial for classification.  \n\n3. The model section contains extensive background on basic Mamba concepts, making it difficult for readers unfamiliar with Mamba to follow. At the same time, readers experienced in time series classification may still find it unclear how the model effectively learns task-relevant features for classification.  \n\n4. Despite the considerable experimental effort and the inclusion of reproducible scripts, all deep learning results in the paper suffer from a **test data leakage issue**. As shown in the provided code (`MambaSL/exp/exp_classification.py`, lines 22–25), the test set is incorrectly used as the validation set:\n\n   ```python\n   self.train_data, self.train_loader = self._get_data(flag='TRAIN')\n   if self.args.is_training:\n       self.vali_data, self.vali_loader = self._get_data(flag='TEST')\n   self.test_data, self.test_loader = self._get_data(flag='TEST')\n\nThis indicates that the test set is used for model validation and hyperparameter selection, resulting in a significant evaluation bias.\n\n5. In Appendix A, the authors mention that the experimental framework is based on the **Time-Series Library (TSLib)** (Wu et al., 2023). While TSLib is widely recognized for fair benchmarking in forecasting tasks, prior research ([1], Section 4.4 “Leaky Baselines”) has shown that TSLib introduces **test data leakage** in classification experiments. Furthermore, several non-deep learning baselines (e.g., Rocket, HC2, Hydra) do not rely on validation sets during training and for model selection. Consequently, using the maximum test performance as the final evaluation metric for MambaSL constitutes an **unfair comparison** with these baselines.\n\n**Reference**\n\n[1] *TOTEM: Tokenized Time Series Embeddings for General Time Series Analysis*, TMLR, 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": "GFONO6u85u", "reviewer": "Reviewer_XLs9", "summary": "The paper proposes MambaSL, a single‑layer Mamba architecture tailored to time‑series classification (TSC). Four hypotheses drive minimal but targeted changes: H1 scales the input Conv1D kernel with sequence length; H2 modularizes time (in)variance of the SSM parameters; H3 disables the skip (D) connection; H4 introduces a multi‑head adaptive pooling readout. The overall block is in Figure 2 (p.5); ablation evidence is in Table 1 (p.8) and Table 2 (p.8–9). On the full 30‑dataset UEA archive, the method attains the top average accuracy and rank among 21 models.", "questions": "+ Could you provide a statistical test (e.g., a Wilcoxon signed-rank test) comparing the performance of MambaSL (with H1) directly against the \"w/o H1\" variant, or should H1 be re-framed in light of this evidence?\n+ Did you try multi‑layer (2–3 layers) MambaSL to confirm single‑layer sufficiency? How does MambaSL perform on variable‑length sequences at test time (beyond the pooling stage)?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "+ Thorough ablations, including eight TI/TV combinations (Table 2) and multiple pooling alternatives (Table 1). \n+ Excellent commitment to reproducibility across all 30 UEA datasets, with promises of public code, checkpoints, and full logs.\n+ The re-evaluation of TSF-origin models, showing they were previously underestimated, is an important finding. The analysis of H2 (time variance) is the most interesting part, showing that for TSC, simpler LTI systems can be better than LTV.", "weaknesses": "+ The novelty of H1–H4 is moderate; each component is a small change rather than a new architectural principle.\n+ The paper's own ablation study (Table 1) shows that H1 (scaling kernel size) is not clearly supported by the average accuracy metric.\n+ The paper's title and focus on a \"single-layer\" model  is not well-justified. Why is one layer sufficient? The paper is missing a ablation study on the effect of model depth (i.e., stacking MambaSL layers).", "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": "MQfjL97h6a", "reviewer": "Reviewer_GbtJ", "summary": "This paper introduces MambaSL, a single-layer Mamba architecture for time series classification. The authors walk through the Mamba architecture in detail, explaining the individual components and the intuition behind how these components related to time series classification. They then propose their method which makes use of these observations in four succinct hypotheses that they use to build MambaSL. They then demonstrate state-of-the-art results across the UEA dataset, a common time series classification dataset, and examine their hypotheses through detailed ablations.", "questions": "- Did authors test MambaSL on datasets with very long samples? One benefit of the Mamba architecture and other SSMs are the ability to capture long contexts; this could be a beneficial demonstration but is also not required.\n- The UMAP showing comparisons of results across datasets is very cool! I’d love to see this UMAP reduction done along the dataset dimension as well to understand which datasets on which MambaSL performs well compared to non-DL vs. DL methods. This is not required at all but could also be an interesting analysis.\n- The authors focus on time series classification in this case, but could this be extended to forecasting?\n- Further, does the work easily extend to irregular time series, where time points are collected in irregular intervals. Can the architecture make use of the irregularity in observations when making predictions?", "limitations": "", "rating": 8, "confidence": 2, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- The authors walk through the Mamba architecture in incredible detail, explaining the components that are necessary to understand their newly proposed architecture. This is greatly appreciated especially for readers with limited knowledge of SSMs. \n- The architecture choices are well-motivated and explained. It’s greatly appreciated that authors are succinct and organized in their description of augmentations to the Mamba architecture, and the ablation results support their decisions. \n- The work puts incredible emphasis on reproducibility and goes to great lengths to discuss the differences in reported vs. optimized accuracy for baseline methods. I found this incredibly rigorous and greatly appreciated, boosting my confidence in the MambaSL performance given their careful considerations of baselines. In addition, the number of baselines included is quite substantial and covers a wide range of methods.\n- This work is overall very principled and novel, with authors clearly laying out the augmentations made on top of the Mamba architecture that are suitable for time series. I think this paves the way for more exploration in this space, and it lays forth a foundation for rigorous and proper benchmarking and principled development of time series models.", "weaknesses": "- The experiments could use errors bars for presentation of results across datasets. Many of the performances are very close to each other on Figure 4, and it’s unclear whether the difference between MambaSL is statistically significant from other methods. In addition, error bars are needed in ablations to understand significance of differences in the dataset.\n- The model seems to not transfer well to variable-length settings, an assumption made implicitly in Hypothesis 1 where the k value is chosen based on the length of the dataset. Can authors comment on the ability of the model in variable-length settings?\n- The UEA dataset, while extensive, contains many small and curated datasets that are not representative of many real-world time series datasets. Did the authors test the method on other datasets for other challenging tasks? The work could use some demonstration on a frontier dataset, such as one released recently that represents a challenging real-world task.", "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)