Pytorch 预测与 Darts，欢迎体验
我想知道 pytorch 预测之间使用哪个包（https://pytorch-forecasting.readthedocs。 io/en/stable/) 或 darts (https://unit8co.github.io/darts/）。我一直在尝试这两种方法，看起来 darts 在编写和风格上更像 sklearn，而 pytorch 预测使用不同的数据类。
欢迎任何比较两者的评论。
我不知道你们中的一些人是否对这两个库进行了性能比较。
提前致谢！
I was wondering which package to use between pytorch forecasting (https://pytorch-forecasting.readthedocs.io/en/stable/) or darts (https://unit8co.github.io/darts/). I have been trying both, it looks like darts is more sklearn-like in its writing and style and pytorch forescasting uses different data classes.
Any comment comparing the two would be welcome.
I don't know if some of you might have carried out a performance comparison between both libraries.
Thanks in advance!
如果你对这篇内容有疑问，欢迎到本站社区发帖提问 参与讨论，获取更多帮助，或者扫码二维码加入 Web 技术交流群。
绑定邮箱获取回复消息
由于您还没有绑定你的真实邮箱，如果其他用户或者作者回复了您的评论，将不能在第一时间通知您！
发布评论
评论（2）
我认为 darts 的最大优势之一是它的 Timeseries 对象，当你熟悉 sklearn 时，它非常像 pandas 并且非常直观。然而，我也确实看到了 pytorch 预测“更好”（更容易）处理分类数据的优势，并且需要更陡峭的学习曲线来理解 pytorch 预测。我想说 pytorch 预测有时优于使用相同模型的 darts。
I think one of the biggest advantage of darts is its Timeseries Object which is very pandas-like and very intuitive when you are familiar with sklearn. However, I also do see the advantage that pytorch-forecasting dealt with categorical data "better" (easier) and it takes a steeper learning curve to understand pytorch-forecasting. I would say pytorch-forecasting sometimes outperform darts using the same model.
如果有帮助的话，我刚刚在 Darts 中为 Temporal Fusion Transformer 模型创建了一个管道，并考虑在 PytorchForecasting 中重建它，原因如下：
如上所述，Darts 中的数据集处理非常好，并且他们抽象了 Pytorch 数据加载器
If helpful, I have just made a pipeline for a Temporal Fusion Transformer model in Darts and am considering rebuilding it in PytorchForecasting for the following reasons:
As mentioned above, the dataset handling in Darts is pretty good and they have abstracted away the Pytorch dataloader