A Simple State Space Model Excels at Multivariate Time Series Classification
Research shows that diagonal state space models (S4D) outperform more complex Mamba architectures in time series classification tasks. The authors propose lightweight variants MS4 and MS4N, which achieve higher accuracy and efficiency on 59 datasets, matching deep learning models with 2x to 10x more parameters.
Article intelligence
Key points
- S4D consistently outperforms Mamba-based variants in accuracy and efficiency on TSC benchmarks.
- Proposed MS4 and MS4N models use simple modifications like linear input projection and channel mixing.
- MS4N matches or surpasses deep learning models with 2x to 10x more parameters while remaining lightweight.
Why it matters
This matters because s4D consistently outperforms Mamba-based variants in accuracy and efficiency on TSC benchmarks.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.27406] A Simple State Space Model Excels at Multivariate Time Series Classification
[Submitted on 7 May 2026]
Title:A Simple State Space Model Excels at Multivariate Time Series Classification
View a PDF of the paper titled A Simple State Space Model Excels at Multivariate Time Series Classification, by Hassan Saadatmand and Geoffrey I. Webb and Hamid Rezatofighi and Mahsa Salehi
View PDF HTML (experimental)
Abstract:Structured state space models (SSMs) have recently emerged as a promising foundation for sequence modeling, with Mamba-based architectures demonstrating strong performance through input-dependent state transitions, albeit at considerable complexity. However, their application to time-series classification (TSC) has been largely limited to Mamba-style architectures, leaving the broader SSM design space underexplored. We present the first systematic study spanning diagonal SSMs (S4D) and input-dependent SSMs (Mamba family) on large-scale TSC benchmarks, asking whether such complexity is necessary for top performance. Our results reveal a surprising finding: S4D consistently outperforms Mamba-based variants in both accuracy and efficiency, challenging the assumption that increased complexity translates to meaningful gains in TSC. Building on this, we introduce MS4, lightweight modifications to S4D via a linear input projection and channel-mixing mechanism, and MS4N, a normalized variant that stabilizes state dynamics with negligible overhead. Evaluated on 59 datasets across MONSTER (up to 60 million samples, 50K timesteps, 82 classes) and the UEA benchmark, against 15 baselines, MS4 and MS4N consistently outperform Mamba-based models while remaining more efficient, and MS4N matches or surpasses competing deep learning models that are roughly 2x and 10x larger in parameters. These results position lightweight structured SSMs as a compelling alternative to scaling complexity for TSC.
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2605.27406 [cs.LG]
(or arXiv:2605.27406v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.27406
arXiv-issued DOI via DataCite
Submission history
From: Hassan Saadatmand [view email] [v1] Thu, 7 May 2026 00:49:25 UTC (19,183 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled A Simple State Space Model Excels at Multivariate Time Series Classification, by Hassan Saadatmand and Geoffrey I. Webb and Hamid Rezatofighi and Mahsa Salehi
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.LG
new | recent | 2026-05
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
IArxiv recommender toggle
IArxiv Recommender (What is IArxiv?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)