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English(EN) ORBIT: Autoregressive Time Series Generation via Next Frequency-Band Prediction

ORBIT框架推进自回归时间序列生成

研究人员推出了一种新颖的深度生成框架ORBIT,旨在合成逼真的时间序列数据。ORBIT解决了学习分层表示和协调异构时间模式生成方面的挑战。它通过两个主要组件实现这一点:用于学习多频表示的频域分层VQ-VAE (FH-VQ-VAE) 和用于结构化生成过程的频域自回归Transformer (FBAR),该过程在添加精细细节之前构建全局结构。实验表明,ORBIT在性能和推理效率方面均达到最先进水平。 AI

影响 该框架可以改进时间序列分析的合成数据生成,可能有助于在真实数据有限的情况下。

排序理由 该集群描述了一篇关于新颖时间序列生成框架的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

ORBIT框架推进自回归时间序列生成

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该集群描述了一篇关于新颖时间序列生成框架的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Chunyi Hou, Xiangfei Qiu, Hanyin Cheng, Chenchen Sun, Yisheng An, Yang Shu, Jilin Hu, Bin Yang ·

    ORBIT:通过预测下一个频段进行自回归时间序列生成

    arXiv:2610.27000v1 Announce Type: new Abstract: Deep generative models provide an effective modern solution to alleviating the problem of data scarcity by synthesizing realistic time series. However, despite the considerable progress achieved by existing deep time series generato…