# Adaptive State-Space Mamba for Real-Time Sensor Data Anomaly Detection

arXiv: https://arxiv.org/abs/2503.22743 · HTML: https://mlsanity.com/paper/2503.22743/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Time series forecasting with SSMs, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 2 of its 5 references that are in the map.
- First posted on arXiv 26 Mar 2025.

## Placed in

- State-space sequence models (S4, Mamba) / [Time series forecasting with SSMs](https://mlsanity.com/ssm/time-series-forecasting-with-ssms/)

## Builds on (most cited first, 2 of 2)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [StyleMamba : State Space Model for Efficient Text-driven Image Style Transfer](https://mlsanity.com/paper/2405.05027/) (2024)

## Abstract (arXiv)

State-space modeling has emerged as a powerful paradigm for sequence analysis in various tasks such as natural language processing, time-series forecasting, and signal processing. In this work, we propose an Adaptive State-Space Mamba (ASSM) framework for real-time sensor data anomaly detection. While state-space models have been previously employed for image processing applications (e.g., style transfer citewang2024stylemamba), our approach leverages the core idea of sequential hidden states to tackle a significantly different domain: detecting anomalies on streaming sensor data. In particular, we introduce an adaptive gating mechanism that dynamically modulates the hidden state update based on contextual and learned statistical cues. This design ensures that our model remains computationally efficient and scalable, even under rapid data arrival rates. Extensive experiments on real-world and synthetic sensor datasets demonstrate that our method achieves superior detection performance compared to existing baselines. Our approach is easily extensible to other time-series tasks that demand rapid and reliable detection capabilities.