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MSDR: Multi-Step Dependency Relation Networks for Spatial Temporal Forecasting

Published: 14 August 2022 Publication History

Abstract

Spatial temporal forecasting plays an important role in improving the quality and performance of Intelligent Transportation Systems. This task is rather challenging due to the complicated and long-range spatial temporal dependencies in traffic network. Existing studies typically employ different deep neural networks to learn the spatial and temporal representations so as to capture the complex and dynamic dependencies. In this paper, we argue that it is insufficient to capture the long-range spatial dependencies from the implicit representations learned by temporal extracting modules. To address this problem, we propose Multi-Step Dependency Relation (MSDR), a brand new variant of recurrent neural network. Instead of only looking at the hidden state from only one latest time step, MSDR explicitly takes those of multiple historical time steps as the input of each time unit. We also develop two strategies to incur the spatial information into the dependency relation embedding between multiple historical time steps and the current one in MSDR. On the basis of it, we propose the Graph-based MSDR (GMSDR) framework to support general spatial temporal forecasting applications by seamlessly integrating graph-based neural networks with MSDR. We evaluate our proposed approach on several popular datasets. The results show that the proposed GMSDR framework outperforms state-of-the-art methods by an obvious margin.

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MP4 File
This is a resentation video for paper MSDR: Multi-Step Dependency Relation Networks for Spatial Temporal Forecasting. MSDR is a brand new variant of recurrent neural network. Instead of only looking at the hidden state from only one latest time step,MSDR explicitly takes those of multiple historical time steps as the input of each time unit. We also develop two strategies to incur the spatial information into the dependency relation embedding between multiple historical time steps and the current one in MSDR.On the basis of it, we propose the Graph-based MSDR (GMSDR) framework to support general spatial temporal forecasting applications by seamlessly integrating graph-based neural networks with MSDR.We evaluate our proposed approach on several popular datasets. The results show that the proposed GMSDR framework outperforms state-of-the-art methods by an obvious margin.

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cover image ACM Conferences
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
August 2022
5033 pages
ISBN:9781450393850
DOI:10.1145/3534678
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Published: 14 August 2022

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Author Tags

  1. multi-step dependency
  2. neural networks
  3. relation embedding
  4. traffic forecasting

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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  • (2025)Spatial-Temporal Fusion Graph Neural Networks With Mixed Adjacency for Weather ForecastingIEEE Access10.1109/ACCESS.2025.353247313(15812-15824)Online publication date: 2025
  • (2025)Traffic flow forecasting based on augmented multi-component recurrent graph attention networkTransportation Letters10.1080/19427867.2025.2450577(1-9)Online publication date: 12-Jan-2025
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