GraceCat123

Really useful information! Thanks!

Yes, that's very true. Success for one robot means success for a whole bunch of robots. However, success for one Olympic athlete does not mean everyone can achieve the same level. That's the main difference.

Totally agree with this! However, it's common for research papers to use exaggerated language to emphasize their breakthroughs and accomplishments. :)

That's really impressive! Are there any limitations or ways to further improve this work? Are the samples shown on the homepage selectively chosen to highlight better performance?

In Machine Learning conference papers, a common approach is to model relationships between variables using Graph Neural Networks (GNNs). Using GNNs is a powerful and flexible way to go. Maybe you can give it a try!

These insights are really awesome! It reminds me of the common aphorism in Statistics: 'All models are wrong, but some are useful.'These insights are really like a wake-up call, thank you!

This example is super classic! XD

Great comments! I've learned a lot from them. I'm just getting started with algorithmic trading and time series modeling, so I appreciate your insights.

Can we employ stochastic processes like the Poisson process to represent irregular data points? Are there any existing models for this?

Are there any good US competitors recommend?