# Graph Neural Networks with Diversity-aware Neighbor Selection and Dynamic Multi-scale Fusion for Multivariate Time Series Forecasting

arXiv: https://arxiv.org/abs/2509.23671 · HTML: https://mlsanity.com/paper/2509.23671/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 28 Sep 2025.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [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)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://mlsanity.com/paper/2201.12740/) (2022)
- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks](https://mlsanity.com/paper/1701.05923/) (2017)
- [Transformers without Normalization](https://mlsanity.com/paper/2503.10622/) (2025)
- [Recurrent Neural Networks (RNNs): A gentle Introduction and Overview](https://mlsanity.com/paper/1912.05911/) (2019)
- [Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2201.04828/) (2022)
- [Attention-based Spatial-Temporal Graph Convolutional Recurrent Networks for Traffic Forecasting](https://mlsanity.com/paper/2302.12973/) (2023)

## Abstract (arXiv)

Recently, numerous deep models have been proposed to enhance the performance of multivariate time series (MTS) forecasting. Among them, Graph Neural Networks (GNNs)-based methods have shown great potential due to their capability to explicitly model inter-variable dependencies. However, these methods often overlook the diversity of information among neighbors, which may lead to redundant information aggregation. In addition, their final prediction typically relies solely on the representation from a single temporal scale. To tackle these issues, we propose a Graph Neural Networks (GNNs) with Diversity-aware Neighbor Selection and Dynamic Multi-scale Fusion (DIMIGNN). DIMIGNN introduces a Diversity-aware Neighbor Selection Mechanism (DNSM) to ensure that each variable shares high informational similarity with its neighbors while maintaining diversity among neighbors themselves. Furthermore, a Dynamic Multi-Scale Fusion Module (DMFM) is introduced to dynamically adjust the contributions of prediction results from different temporal scales to the final forecasting result. Extensive experiments on real-world datasets demonstrate that DIMIGNN consistently outperforms prior methods.