# Time series forecasting with SSMs

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

## Key facts

- Time series forecasting with SSMs: 88 papers on arXiv since 2022 (data through 14 Oct 2026).
- 17 of its 88 mapped papers (19%) cite “TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting” (2024, arXiv 2403.09898).
- 17 of its 88 mapped papers (19%) cite “Is Mamba Effective for Time Series Forecasting?” (2024, arXiv 2403.11144).
- 5 new papers 18 Sep–14 Oct.

## Most cited here

- [TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting](https://mlsanity.com/paper/2403.09898/) (2024), cited by 17 papers here
- [Is Mamba Effective for Time Series Forecasting?](https://mlsanity.com/paper/2403.11144/) (2024), cited by 17 papers here
- [Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting](https://mlsanity.com/paper/2404.15772/) (2024), cited by 4 papers here
- [MambaTS: Improved Selective State Space Models for Long-term Time Series Forecasting](https://mlsanity.com/paper/2405.16440/) (2024), cited by 4 papers here
- [State-space models are accurate and efficient neural operators for dynamical systems](https://mlsanity.com/paper/2409.03231/) (2024), cited by 4 papers here
- [MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection](https://mlsanity.com/paper/2404.06564/) (2024), cited by 3 papers here
- [TSCMamba: Mamba Meets Multi-View Learning for Time Series Classification](https://mlsanity.com/paper/2406.04419/) (2024), cited by 3 papers here
- [CMamba: Channel Correlation Enhanced State Space Models for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2406.05316/) (2024), cited by 3 papers here
- [FMamba: Mamba based on Fast-attention for Multivariate Time-series Forecasting](https://mlsanity.com/paper/2407.14814/) (2024), cited by 3 papers here
- [Mamba Neural Operator: Who Wins? Transformers vs. State-Space Models for PDEs](https://mlsanity.com/paper/2410.02113/) (2024), cited by 3 papers here

## New (18 Sep–14 Oct)

- [HAN-Mamba: Hierarchical Selective State Space Networks for Multi-Scale Financial Volatility Forecasting](https://mlsanity.com/paper/2610.10323/) (2026-10-07)
- [GeoPrior-Mamba: Structured Process Priors with Mamba for Fine-Resolution XCO2 Reconstruction](https://mlsanity.com/paper/2610.09456/) (2026-10-07)
- [LogSig-SSM: Time-Series Modelling with Multi-Scale Log-Signature Compression for State-Space Models](https://mlsanity.com/paper/2610.05051/) (2026-10-04)
- [State-Space Unlearning for Non-Stationary Bias in Land Surface Forecasting](https://mlsanity.com/paper/2610.02248/) (2026-09-30)
- [Physics and Data Driven Transformer-Mamba Framework for Flow Field](https://mlsanity.com/paper/2609.29087/) (2026-09-24)