求问TimeNet（时序分析ResNet等价RNN模型）预训练权重与架构获取方式

Hey there, I get the frustration—TimeNet (the ResNet-equivalent RNN for time series analysis) has solid published work behind it, but finding the actual model weights and architecture details can feel like hunting for a needle in a haystack. Let’s break down your options:

• Check the authors' personal pages/GitHub repos
  Many researchers host supplementary code, weights, or architecture specs on their personal websites or GitHub accounts, even if it’s not explicitly linked in the paper. Try searching for the lead authors’ names alongside "TimeNet"—you might stumble upon an unlisted repo or a project page with the resources you need.

• Reach out directly to the paper authors
  This is often the most reliable route. Send a polite, concise email explaining your use case (e.g., academic research, personal project) and ask if they’re willing to share the pre-trained weights or architecture files. Most researchers are happy to help the community, especially if you’re using the work for non-commercial purposes.

• Check academic community platforms
  Head to platforms like ResearchGate or the paper’s Google Scholar page—sometimes authors upload supplementary materials (including code/weight links) in the paper’s attachments, or other users might have asked the same question and gotten a shareable resource in the comments.

• Consider reproducing the model yourself
  If you can’t find pre-trained weights, the paper should outline the full architecture details (layer configurations, hyperparameters, etc.). You can use frameworks like PyTorch or TensorFlow to build the model from scratch, then pre-train it on public time series datasets like the UCR/UCI Time Series Archive. While it’s extra work, this gives you full control over the model and weights.

As for public availability: From what I’ve seen, pre-trained TimeNet weights aren’t widely publicly hosted right now. It’s likely the authors haven’t formally released them, or they’re tied to internal research projects. That said, the above steps should give you a good shot at accessing what you need.

内容的提问来源于stack exchange，提问作者misterte