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WenjieDu / SAITS

The official PyTorch implementation of the paper "SAITS: Self-Attention-based Imputation for Time Series". A fast and state-of-the-art (SOTA) deep-learning neural network model for efficient time-series imputation (impute multivariate incomplete time series containing NaN missing data/values with machine learning). https://arxiv.org/abs/2202.08516
https://doi.org/10.1016/j.eswa.2023.119619
MIT License
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训练步长可以动态调整吗？ #34

Closed xuanhaodong closed 6 months ago

xuanhaodong commented 7 months ago

你好，根据给出的example.py中的saits = SAITS(n_steps=48, n_features=37, n_layers=2, d_model=256, d_inner=128, n_heads=4, d_k=64, d_v=64, dropout=0.1, epochs=10)，可以看到n_steps被设置为48，因为example中给定的数据集中每个RecordID都有48个样本。 但我的数据集中每个RecordID对应的样本数是不固定的，比如1个，7个，甚至216个，这样的话我把n_steps参数设置为最大的RecordID对应数目，比如216，这会是可行的吗？或者有没有其它方案。十分感谢！

WenjieDu commented 7 months ago

Hi there,

Thank you so much for your attention to SAITS! If you find SAITS is helpful to your work, please star⭐️ this repository. Your star is your recognition, which can let others notice SAITS. It matters and is definitely a kind of contribution.

I have received your message and will respond ASAP. Thank you again for your patience! 😃

Best,
Wenjie

WenjieDu commented 7 months ago

你好，目前不支持，这个feature我们在PyPOTS的issue#139中讨论过，目前优先级不高，但后续会在PyPOTS中更新，欢迎参与讨论。你可以在github上follow我并加入我们的微信社区以获取与PyPOTS相关的最新消息

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