# Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting

arXiv: https://arxiv.org/abs/2405.10800 · HTML: https://mlsanity.com/paper/2405.10800/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 71 references that are in the map.
- First posted on arXiv 17 May 2024.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://mlsanity.com/paper/2005.11650/) (2020)
- [Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting](https://mlsanity.com/paper/2007.02842/) (2020)
- [Transformers in Time Series: A Survey](https://mlsanity.com/paper/2202.07125/) (2022)
- [GMAN: A Graph Multi-Attention Network for Traffic Prediction](https://mlsanity.com/paper/1911.08415/) (2019)
- [Dynamic Filter Networks](https://mlsanity.com/paper/1605.09673/) (2016)
- [Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting](https://mlsanity.com/paper/2103.07719/) (2021)
- [Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2012.09641/) (2020)
- [Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2106.12931/) (2021)
- [Discrete Graph Structure Learning for Forecasting Multiple Time Series](https://mlsanity.com/paper/2101.06861/) (2021)
- [Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2208.05233/) (2022)
- [Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis](https://mlsanity.com/paper/2310.06119/) (2023)
- [Learning feed-forward one-shot learners](https://mlsanity.com/paper/1606.05233/) (2016)
- [Spatio-Temporal Meta-Graph Learning for Traffic Forecasting](https://mlsanity.com/paper/2211.14701/) (2022)
- [Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction](https://mlsanity.com/paper/2212.04475/) (2022)
- [DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction](https://mlsanity.com/paper/2108.09091/) (2021)
- [Coupled Layer-wise Graph Convolution for Transportation Demand Prediction](https://mlsanity.com/paper/2012.08080/) (2020)
- [Spatio-temporal Diffusion Point Processes](https://mlsanity.com/paper/2305.12403/) (2023)
- [Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment](https://mlsanity.com/paper/2309.13378/) (2023)
- [Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer](https://mlsanity.com/paper/2205.13947/) (2022)
- [Historical Inertia: A Neglected but Powerful Baseline for Long Sequence Time-series Forecasting](https://mlsanity.com/paper/2103.16349/) (2021)
- [Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting](https://mlsanity.com/paper/2110.10380/) (2021)
- [Meta Multi-Task Learning for Sequence Modeling](https://mlsanity.com/paper/1802.08969/) (2018)

## Built on by (most cited first, 18 of 18)

- [Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts](https://mlsanity.com/paper/2409.16040/) (2024)
- [Expand and Compress: Exploring Tuning Principles for Continual Spatio-Temporal Graph Forecasting](https://mlsanity.com/paper/2410.12593/) (2024)
- [How Different from the Past? Spatio-Temporal Time Series Forecasting with Self-Supervised Deviation Learning](https://mlsanity.com/paper/2510.04908/) (2025)
- [A Gravity-informed Spatiotemporal Transformer for Human Activity Intensity Prediction](https://mlsanity.com/paper/2506.13678/) (2025)
- [Enhancing Ride-Hailing Forecasting at DiDi with Multi-View Geospatial Representation Learning from the Web](https://mlsanity.com/paper/2602.10502/) (2026)
- [Efficient Traffic Forecasting on Large-Scale Road Network by Regularized Adaptive Graph Convolution](https://mlsanity.com/paper/2506.07179/) (2025)
- [Nested Spatio-Temporal Time Series Forecasting](https://mlsanity.com/paper/2605.16447/) (2026)
- [HiMoE: Heterogeneity-Informed Mixture-of-Experts for Fair Spatial-Temporal Forecasting](https://mlsanity.com/paper/2412.00316/) (2024)
- [Accurate and Efficient Multivariate Time Series Forecasting via Offline Clustering](https://mlsanity.com/paper/2505.05738/) (2025)
- [Proxy Reconstruction Pre-training for Ramp Flow Prediction at Highway Interchanges](https://mlsanity.com/paper/2510.03381/) (2025)
- [Efficient Linear Attention for Multivariate Time Series Modeling via Entropy Equality](https://mlsanity.com/paper/2511.03190/) (2025)
- [Towards Resilient Transportation: A Conditional Transformer for Accident-Informed Traffic Forecasting](https://mlsanity.com/paper/2512.09398/) (2025)
- [MP3: Multi-Period Pattern Pre-training for Spatio-Temporal Forecasting](https://mlsanity.com/paper/2606.13119/) (2026)
- [Behaviour-Conditioned Neural Processes for Adaptive Residential Short-Term Load Forecasting](https://mlsanity.com/paper/2607.16168/) (2026)
- [Fourier Geometric Wind Power Forecasting with Numerical Weather Prediction](https://mlsanity.com/paper/2607.17095/) (2026)
- [TimeES: Probabilistic and Deterministic Time Series Forecasting via Evolutionary Spectra](https://mlsanity.com/paper/2609.32384/) (2026)
- [Addressing Spatial Indistinguishability in Spatiotemporal Prediction via Optimal Transport-Guided Masking](https://mlsanity.com/paper/2609.35021/) (2026)
- [AdaST: Adaptive Coupling for Spatial-Temporal Forecasting](https://mlsanity.com/paper/2609.36119/) (2026)

## Abstract (arXiv)

Spatiotemporal time series forecasting plays a key role in a wide range of real-world applications. While significant progress has been made in this area, fully capturing and leveraging spatiotemporal heterogeneity remains a fundamental challenge. Therefore, we propose a novel Heterogeneity-Informed Meta-Parameter Learning scheme. Specifically, our approach implicitly captures spatiotemporal heterogeneity through learning spatial and temporal embeddings, which can be viewed as a clustering process. Then, a novel spatiotemporal meta-parameter learning paradigm is proposed to learn spatiotemporal-specific parameters from meta-parameter pools, which is informed by the captured heterogeneity. Based on these ideas, we develop a Heterogeneity-Informed Spatiotemporal Meta-Network (HimNet) for spatiotemporal time series forecasting. Extensive experiments on five widely-used benchmarks demonstrate our method achieves state-of-the-art performance while exhibiting superior interpretability. Our code is available at https://github.com/XDZhelheim/HimNet.