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English(EN) MambaTS: Improved Selective State Space Models for Long-term Time Series Forecasting

MambaTS框架在不使用自注意力机制的情况下提供了改进的时间序列预测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了MambaTS，一个利用选择性状态空间模型作为其骨干的长期时间序列预测新框架。与传统的Transformer不同，MambaTS避免了自注意力机制的二次方复杂度。它引入了变量感知扫描（VAST）来学习变量间关系并确定最优扫描顺序。在八个数据集上的实验表明，MambaTS取得了具有竞争力或最先进的结果。 AI

影响 为长期时间序列预测提供了比Transformer更高效的替代方案，有可能提高依赖于顺序数据的应用程序的性能。

排序理由 该集群包含一篇详细介绍时间序列预测新模型架构的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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MambaTS框架在不使用自注意力机制的情况下提供了改进的时间序列预测

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该集群包含一篇详细介绍时间序列预测新模型架构的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Xiuding Cai, Xueyao Wang, Yaoyao Zhu, Yu Yao ·

    MambaTS：用于长期时间序列预测的改进型选择性状态空间模型

    arXiv:2405.16440v2 Announce Type: replace-cross Abstract: In recent years, Transformers have become the de-facto architecture for long-term time series forecasting (LTSF), yet they face challenges associated with the self-attention mechanism, including quadratic complexity and pe…

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