# Mamba Adaptive Anomaly Transformer with association discrepancy for time series

arXiv: https://arxiv.org/abs/2502.07858 · HTML: https://mlsanity.com/paper/2502.07858/

## Key facts

- Cited by 5 papers in the map (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 21 of its 44 references that are in the map.
- First posted on arXiv 11 Feb 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, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [Reformer: The Efficient Transformer](https://mlsanity.com/paper/2001.04451/) (2020)
- [Generating Long Sequences with Sparse Transformers](https://mlsanity.com/paper/1904.10509/) (2019)
- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy](https://mlsanity.com/paper/2110.02642/) (2021)
- [Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications](https://mlsanity.com/paper/1802.03903/) (2018)
- [LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection](https://mlsanity.com/paper/1607.00148/) (2016)
- [MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks](https://mlsanity.com/paper/1901.04997/) (2019)
- [DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection](https://mlsanity.com/paper/2306.10347/) (2023)
- [U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging](https://mlsanity.com/paper/1910.11162/) (2019)
- [Local Evaluation of Time Series Anomaly Detection Algorithms](https://mlsanity.com/paper/2206.13167/) (2022)
- [Memory-efficient Transformers via Top-k Attention](https://mlsanity.com/paper/2106.06899/) (2021)
- [AnomalyBERT: Self-Supervised Transformer for Time Series Anomaly Detection using Data Degradation Scheme](https://mlsanity.com/paper/2305.04468/) (2023)
- [Network Traffic Anomaly Detection Using Recurrent Neural Networks](https://mlsanity.com/paper/1803.10769/) (2018)
- [DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2310.08800/) (2023)
- [Multi-Task Time Series Forecasting With Shared Attention](https://mlsanity.com/paper/2101.09645/) (2021)
- [Just In Time Transformers](https://mlsanity.com/paper/2410.16881/) (2024)

## Built on by (most cited first, 5 of 5)

- [RAPT: Model-Predictive Out-of-Distribution Detection and Failure Diagnosis for Sim-to-Real Humanoid Deployment](https://mlsanity.com/paper/2602.01515/) (2026)
- [Fourier-KAN-Mamba: A Novel State-Space Equation Approach for Time-Series Anomaly Detection](https://mlsanity.com/paper/2511.15083/) (2025)
- [Transcending the Annotation Bottleneck: AI-Powered Discovery in Biology and Medicine](https://mlsanity.com/paper/2602.20100/) (2026)
- [Fast and Accurate Anomaly Detection in Time Series](https://mlsanity.com/paper/2607.02046/) (2026)
- [DR-net-Mamba: Selective State-Space Modeling for Long-Range ECG Time-Series Denoising](https://mlsanity.com/paper/2609.35634/) (2026)

## Abstract (arXiv)

Anomaly detection in time series is essential for industrial monitoring and environmental sensing, yet distinguishing anomalies from complex patterns remains challenging. Existing methods like the Anomaly Transformer and DCdetector have progressed, but they face limitations such as sensitivity to short-term contexts and inefficiency in noisy, non-stationary environments. To overcome these issues, we introduce MAAT, an improved architecture that enhances association discrepancy modeling and reconstruction quality. MAAT features Sparse Attention, efficiently capturing long-range dependencies by focusing on relevant time steps, thereby reducing computational redundancy. Additionally, a Mamba-Selective State Space Model is incorporated into the reconstruction module, utilizing a skip connection and Gated Attention to improve anomaly localization and detection performance. Extensive experiments show that MAAT significantly outperforms previous methods, achieving better anomaly distinguishability and generalization across various time series applications, setting a new standard for unsupervised time series anomaly detection in real-world scenarios.