MambaSL: Exploring Single-Layer Mamba for Time Series Classification

arXiv:2604.15174 · cs.LG, cs.AI · Submitted 2026-04-16 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-04-16

Updated: 2026-09-27

Code: https://github.com/state-spaces/mamba

Terminology

• Accuracy — 82× in this paper · explained in 1 episode
• Transformer — 22× in this paper · explained in 2 episodes
• Benchmark — 18× in this paper · explained in 2 episodes
• Ablation — 13× in this paper · explained in 1 episode
• Benchmarking — 8× in this paper · explained in 4 episodes
• Aggregation — 6× in this paper · explained in 1 episode
• Time Series Forecasting — 5× in this paper · explained in 3 episodes
• Foundation Model — 4× in this paper · explained in 3 episodes
• Foundation Models — 4× in this paper · explained in 7 episodes
• Core — 3× in this paper · explained in 2 episodes
• State Space Models (SSMs) — 3× in this paper · explained in 4 episodes
• University of East Anglia — 3× in this paper
• Adaptation — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Generalizability — 2× in this paper · explained in 6 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Adaptive Weighting — 1× in this paper · explained in 1 episode
• Aggregation Function — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Dynamic Time Warping (DTW) — 1× in this paper · explained in 2 episodes
• Feature Engineering — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• The UEA multivariate time series classification archive, 2018
• Neural Machine Translation by Jointly Learning to Align and Translate
• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• ETSformer: Exponential Smoothing Transformers for Time-series Forecasting

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