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  • 时间序列预测还能在进步吗？ - 知乎
    在实现了市面上几个主要的模型例如 PatchTST，FITS，TimesNet，iTransformer之后，感觉23,24年的时间序列…
  • 如何看待长时序预测最新的Transformer模型PETformer击败 . . .
    PRformer整体架构 在8个真实时间序列数据集上达到了SOTA性能，同时大幅领先线性模型预测器。 得益于PRE，PRformer时空复杂度随序列长度线性增长。 相比现有Transformer SOTA基线PatchTST，PRformer的运行时间和内存占用显著降低。
  • A Time Series is Worth 64 Words: Long-term Forecasting with. . .
    Our channel-independent patch time series Transformer (PatchTST) can improve the long-term forecasting accuracy significantly when compared with that of SOTA Transformer-based models We also apply our model to self-supervised pre-training tasks and attain excellent fine-tuning performance, which outperforms supervised training on large datasets
  • TSMixer: An All-MLP Architecture for Time Series Fore-casting
    SMixer exhibits similar performance to TMix-Only and PatchTST It significantly outperforms state-of-the-art multivariate models and achieves competitive performan e compared to PatchTST, the state-of-the-art univariate model TSMixer is the only multivariate model that is competitive with univariate models with all other multivariat
  • [ICLR 2023] PatchTST Rebuttal：如何捍卫简单有效的 Idea . . .
    结果显示，即使如此 PatchTST 仍有显著优势。 关于 Instance Normalization 的消融实验： 新增 Table 11，显示即使不使用 InstanceNorm（或 RevIN），PatchTST 仍优于现有 Transformer 模型，证明主提升来自 patch 和 channel-independence。 关于鲁棒性测试：
  • A T S WORTH 64 WORDS - OpenReview
    PatchTST in Table 3 PatchTST 64 implies the number of input patches is 64, which uses the look back window L = 512 PatchTST 42 means the number of input patches is 42, which has the default look
  • 数据STUDIO - 知乎
    本文首发于原创公众号「数据STUDIO」： 时间序列数据处理，不再使用pandas 本文中，云朵君和大家一起学习了五个Python时间序列库，包括Darts和Gluonts库的数据结构，以及如何在这些库中转换pandas数据框，并将其转换回pandas。 Pandas DataFrame通常用于处理时间序列数据。对于单变量时…
  • Revisiting Long-term Time Series Forecasting: An . . . - OpenReview
    Long-term time series forecasting (LTSF) has gained significant attention in recent years While there are various specialized designs for capturing temporal dependency, previous studies have
  • 只此青绿 - 知乎
    草率打工人 回答数 6，获得 97 次赞同
  • 1I LEARNING TO EMBED TIME SERIES PATCHES INDEPENDEN - OpenReview
    ABSTRACT Masked time series modeling has recently gained much attention as a self-supervised representation learning strategy for time series Inspired by masked image modeling in computer vision, recent works first patchify and partially mask out time series, and then train Transformers to capture the dependencies between patches by predicting masked patches from unmasked patches However, we





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