# MLPST: MLP is All You Need for Spatio-Temporal Prediction

arXiv: https://arxiv.org/abs/2309.13363 · HTML: https://mlsanity.com/paper/2309.13363/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 56 references that are in the map.
- First posted on arXiv 23 Sep 2023.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Transformer in Transformer](https://mlsanity.com/paper/2103.00112/) (2021)
- [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://mlsanity.com/paper/2005.11650/) (2020)
- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [ResMLP: Feedforward networks for image classification with data-efficient training](https://mlsanity.com/paper/2105.03404/) (2021)
- [Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction](https://mlsanity.com/paper/1610.00081/) (2016)
- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [Discrete Graph Structure Learning for Forecasting Multiple Time Series](https://mlsanity.com/paper/2101.06861/) (2021)
- [CycleMLP: A MLP-like Architecture for Dense Prediction](https://mlsanity.com/paper/2107.10224/) (2021)
- [Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention](https://mlsanity.com/paper/1911.07757/) (2019)
- [STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting](https://mlsanity.com/paper/1905.10069/) (2019)
- [Bike Flow Prediction with Multi-Graph Convolutional Networks](https://mlsanity.com/paper/1807.10934/) (2018)
- [Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?](https://mlsanity.com/paper/2109.05422/) (2021)
- [A Battle of Network Structures: An Empirical Study of CNN, Transformer, and MLP](https://mlsanity.com/paper/2108.13002/) (2021)
- [Spatio-Temporal Data Mining: A Survey of Problems and Methods](https://mlsanity.com/paper/1711.04710/) (2017)
- [Deep-learning Architecture for Short-term Passenger Flow Forecasting in Urban Rail Transit](https://mlsanity.com/paper/1912.12563/) (2019)
- [Attentive Crowd Flow Machines](https://mlsanity.com/paper/1809.00101/) (2018)
- [PCNN: Deep Convolutional Networks for Short-term Traffic Congestion Prediction](https://mlsanity.com/paper/2003.07033/) (2020)
- [Resting state fMRI functional connectivity-based classification using a convolutional neural network architecture](https://mlsanity.com/paper/1707.06682/) (2017)
- [AutoSTL: Automated Spatio-Temporal Multi-Task Learning](https://mlsanity.com/paper/2304.09174/) (2023)
- [Crime in Urban Areas: A Data Mining Perspective](https://mlsanity.com/paper/1804.08159/) (2018)

## Built on by (most cited first, 22 of 22)

- [UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction](https://mlsanity.com/paper/2402.11838/) (2024)
- [G3: An Effective and Adaptive Framework for Worldwide Geolocalization Using Large Multi-Modality Models](https://mlsanity.com/paper/2405.14702/) (2024)
- [Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and Human-Like Reasoning Framework](https://mlsanity.com/paper/2502.13759/) (2025)
- [RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Data](https://mlsanity.com/paper/2402.10487/) (2024)
- [POI-Enhancer: An LLM-based Semantic Enhancement Framework for POI Representation Learning](https://mlsanity.com/paper/2502.10038/) (2025)
- [GARLIC: GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching](https://mlsanity.com/paper/2408.10286/) (2024)
- [Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach](https://mlsanity.com/paper/2312.01699/) (2023)
- [ODMixer: Fine-grained Spatial-temporal MLP for Metro Origin-Destination Prediction](https://mlsanity.com/paper/2404.15734/) (2024)
- [Learning to Wander: Improving the Global Image Geolocation Ability of LMMs via Actionable Reasoning](https://mlsanity.com/paper/2603.10463/) (2026)
- [STM3: Mixture of Multiscale Mamba for Long-Term Spatio-Temporal Time-Series Prediction](https://mlsanity.com/paper/2508.12247/) (2025)
- [HieraMix: A Hierarchical MLP-Mixer for Large-Scale Traffic Forecasting](https://mlsanity.com/paper/2512.07854/) (2025)
- [Do We Really Need Adaptive Global Spatial Attention for Traffic Forecasting?](https://mlsanity.com/paper/2607.12462/) (2026)
- [Three Pillars Towards Next-Generation Routing System](https://mlsanity.com/paper/2409.11412/) (2024)
- [An Event-centric Framework for Predicting Crime Hotspots with Flexible Time Intervals](https://mlsanity.com/paper/2411.01134/) (2024)
- [Collaborative Deterministic-Probabilistic Forecasting for Diverse Spatiotemporal Systems](https://mlsanity.com/paper/2502.11013/) (2025)
- [Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations](https://mlsanity.com/paper/2506.21682/) (2025)
- [NLP4Neuro: Sequence-to-sequence learning for neural population decoding](https://mlsanity.com/paper/2507.02264/) (2025)
- [Channel-Independent Federated Traffic Prediction](https://mlsanity.com/paper/2508.04517/) (2025)
- [Root Cause Analysis of Hydrogen Bond Separation in Spatio-Temporal Molecular Dynamics using Causal Models](https://mlsanity.com/paper/2508.12500/) (2025)
- [We Need a More Robust Classifier: Dual Causal Learning Empowers Domain-Incremental Time Series Classification](https://mlsanity.com/paper/2601.10312/) (2026)
- [From Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Prediction](https://mlsanity.com/paper/2606.09392/) (2026)
- [TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting](https://mlsanity.com/paper/2607.06349/) (2026)

## Abstract (arXiv)

Traffic prediction is a typical spatio-temporal data mining task and has great significance to the public transportation system. Considering the demand for its grand application, we recognize key factors for an ideal spatio-temporal prediction method: efficient, lightweight, and effective. However, the current deep model-based spatio-temporal prediction solutions generally own intricate architectures with cumbersome optimization, which can hardly meet these expectations. To accomplish the above goals, we propose an intuitive and novel framework, MLPST, a pure multi-layer perceptron architecture for traffic prediction. Specifically, we first capture spatial relationships from both local and global receptive fields. Then, temporal dependencies in different intervals are comprehensively considered. Through compact and swift MLP processing, MLPST can well capture the spatial and temporal dependencies while requiring only linear computational complexity, as well as model parameters that are more than an order of magnitude lower than baselines. Extensive experiments validated the superior effectiveness and efficiency of MLPST against advanced baselines, and among models with optimal accuracy, MLPST achieves the best time and space efficiency.