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POLLA: Enhancing the Local Structure Awareness in Long Sequence Spatial-temporal Modeling

Authors:

Jianxin LiAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 12, Issue 6

Article No.: 69, Pages 1 - 24

https://doi.org/10.1145/3447987

Published: 29 November 2021 Publication History

Abstract

The spatial-temporal modeling on long sequences is of great importance in many real-world applications. Recent studies have shown the potential of applying the self-attention mechanism to improve capturing the complex spatial-temporal dependencies. However, the lack of underlying structure information weakens its general performance on long sequence spatial-temporal problem. To overcome this limitation, we proposed a novel method, named the Proximity-aware Long Sequence Learning framework, and apply it to the spatial-temporal forecasting task. The model substitutes the canonical self-attention by leveraging the proximity-aware attention, which enhances local structure clues in building long-range dependencies with a linear approximation of attention scores. The relief adjacency matrix technique can utilize the historical global graph information for consistent proximity learning. Meanwhile, the reduced decoder allows for fast inference in a non-autoregressive manner. Extensive experiments are conducted on five large-scale datasets, which demonstrate that our method achieves state-of-the-art performance and validates the effectiveness brought by local structure information.

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Cited By

Li ZGao ZZhang XZhang GXu L(2024)Time-aware personalized graph convolutional network for multivariate time series forecastingExpert Systems with Applications10.1016/j.eswa.2023.122471240(122471)Online publication date: Apr-2024
https://doi.org/10.1016/j.eswa.2023.122471
Li ZGao ZZhang GLiu JXu L(2024)Dynamic personalized graph neural network with linear complexity for multivariate time series forecastingEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107291127:PAOnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107291
Li JZhu TZhou HSun QJiang CZhang SHu C(2022)AIQoSer: Building the efficient Inference-QoS for AI Services2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)10.1109/IWQoS54832.2022.9812905(1-10)Online publication date: 10-Jun-2022
https://doi.org/10.1109/IWQoS54832.2022.9812905
Show More Cited By

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POLLA: Enhancing the Local Structure Awareness in Long Sequence Spatial-temporal Modeling

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 12, Issue 6

December 2021

356 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3501281

Editor:
Huan Liu
Arizona State University, USA

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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Publication History

Published: 29 November 2021

Accepted: 01 January 2021

Revised: 01 December 2020

Received: 01 October 2020

Published in TIST Volume 12, Issue 6

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Natural Science Foundation of China
State Key Laboratory of Software Development Environment
CAAI-Huawei MindSpore Open Fund

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Cited By

Li ZGao ZZhang XZhang GXu L(2024)Time-aware personalized graph convolutional network for multivariate time series forecastingExpert Systems with Applications10.1016/j.eswa.2023.122471240(122471)Online publication date: Apr-2024
https://doi.org/10.1016/j.eswa.2023.122471
Li ZGao ZZhang GLiu JXu L(2024)Dynamic personalized graph neural network with linear complexity for multivariate time series forecastingEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107291127:PAOnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107291
Li JZhu TZhou HSun QJiang CZhang SHu C(2022)AIQoSer: Building the efficient Inference-QoS for AI Services2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)10.1109/IWQoS54832.2022.9812905(1-10)Online publication date: 10-Jun-2022
https://doi.org/10.1109/IWQoS54832.2022.9812905
Papachary BAmru MRama Kishore Reddy S(2021)An Effective Segmentation of Tissues from MR Brain ImagesJournal of Physics: Conference Series10.1088/1742-6596/1964/6/0620291964:6(062029)Online publication date: 1-Jul-2021
https://doi.org/10.1088/1742-6596/1964/6/062029

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