# Integration of Mamba and Transformer -- MAT for Long-Short Range Time Series Forecasting with Application to Weather Dynamics

arXiv: https://arxiv.org/abs/2409.08530 · HTML: https://mlsanity.com/paper/2409.08530/

## Key facts

- Cited by 7 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 26 of its 34 references that are in the map.
- First posted on arXiv 13 Sep 2024.

## 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, 26 of 26)

- [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)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [iTransformer: Inverted Transformers Are Effective for Time Series Forecasting](https://mlsanity.com/paper/2310.06625/) (2023)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [One Fits All:Power General Time Series Analysis by Pretrained LM](https://mlsanity.com/paper/2302.11939/) (2023)
- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [Jamba: A Hybrid Transformer-Mamba Language Model](https://mlsanity.com/paper/2403.19887/) (2024)
- [Multimodal Learning with Transformers: A Survey](https://mlsanity.com/paper/2206.06488/) (2022)
- [SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction](https://mlsanity.com/paper/2106.09305/) (2021)
- [Long-term Forecasting with TiDE: Time-series Dense Encoder](https://mlsanity.com/paper/2304.08424/) (2023)
- [A Survey of Visual Transformers](https://mlsanity.com/paper/2111.06091/) (2021)
- [Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks](https://mlsanity.com/paper/2402.04248/) (2024)
- [A Clockwork RNN](https://mlsanity.com/paper/1402.3511/) (2014)
- [TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting](https://mlsanity.com/paper/2403.09898/) (2024)
- [Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges](https://mlsanity.com/paper/2404.16112/) (2024)
- [A Survey on Visual Mamba](https://mlsanity.com/paper/2404.15956/) (2024)
- [Learning Transformer Programs](https://mlsanity.com/paper/2306.01128/) (2023)
- [Locating and Editing Factual Associations in Mamba](https://mlsanity.com/paper/2404.03646/) (2024)
- [Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformers](https://mlsanity.com/paper/2406.12199/) (2024)
- [APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking](https://mlsanity.com/paper/2406.14449/) (2024)
- [Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approach](https://mlsanity.com/paper/2408.06634/) (2024)
- [ZeroPrompt: Streaming Acoustic Encoders are Zero-Shot Masked LMs](https://mlsanity.com/paper/2305.10649/) (2023)
- [Fostc3net:A Lightweight YOLOv5 Based On the Network Structure Optimization](https://mlsanity.com/paper/2403.13703/) (2024)
- [Utilizing the LightGBM Algorithm for Operator User Credit Assessment Research](https://mlsanity.com/paper/2403.14483/) (2024)

## Built on by (most cited first, 7 of 7)

- [Multimodal Conditioned Diffusive Time Series Forecasting](https://mlsanity.com/paper/2504.19669/) (2025)
- [Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives](https://mlsanity.com/paper/2506.24124/) (2025)
- [Automatic selection of the best neural architecture for time series forecasting](https://mlsanity.com/paper/2501.12215/) (2025)
- [Moving Beyond Functional Connectivity: Time-Series Modeling for fMRI-Based Brain Disorder Classification](https://mlsanity.com/paper/2602.08262/) (2026)
- [Mitigating Knowledge Conflicts in Language Model-Driven Question Answering](https://mlsanity.com/paper/2411.11344/) (2024)
- [Optimization and Scalability of Collaborative Filtering Algorithms in Large Language Models](https://mlsanity.com/paper/2412.18715/) (2024)
- [El Nino Prediction Based on Weather Forecast and Geographical Time-series Data](https://mlsanity.com/paper/2604.04998/) (2026)

## Abstract (arXiv)

Long-short range time series forecasting is essential for predicting future trends and patterns over extended periods. While deep learning models such as Transformers have made significant strides in advancing time series forecasting, they often encounter difficulties in capturing long-term dependencies and effectively managing sparse semantic features. The state-space model, Mamba, addresses these issues through its adept handling of selective input and parallel computing, striking a balance between computational efficiency and prediction accuracy. This article examines the advantages and disadvantages of both Mamba and Transformer models, and introduces a combined approach, MAT, which leverages the strengths of each model to capture unique long-short range dependencies and inherent evolutionary patterns in multivariate time series. Specifically, MAT harnesses the long-range dependency capabilities of Mamba and the short-range characteristics of Transformers. Experimental results on benchmark weather datasets demonstrate that MAT outperforms existing comparable methods in terms of prediction accuracy, scalability, and memory efficiency.