thanks sir , great exposition , however am wondering as to how one go about adapting such more dynamic approach to a case where the values are time series values are non stationary (main properties change over time , i.e. mean , variance and covariance ) on top of that such type series exhibit heavier tails i.e. probability of having very large values or very small values tend to be higher relative to normal distribution , so your input is highly appreciated keep up the good work
(Vincent here) I might recommend playing with the notebook to unravel this one. But I'll keep this idea in mind because it might be an interesting appendix short later.
Great videos! Super helpful and I learn something new all the time. Keep up the good work!
Just wanted to say I love these kind of videos - early days for the channel just wanted to give that positive feedback to continue
(Vincent here) Happy to hear it. Plenty more is on the way!
Excellent video, I really like how you explain complex concepts and techniques
Great video! I think you nailed the explanation. It is nice to see how to use Jupyter widgets as tools to explain ML-related concepts.
Thanks!
Speaking of widgets. Seen these?
th-cam.com/video/STPv0jSAQEk/w-d-xo.html
th-cam.com/video/goaBFxGhp6Y/w-d-xo.html
thanks sir , great exposition , however am wondering as to how one go about adapting such more dynamic approach to a case where the values are time series values are non stationary (main properties change over time , i.e. mean , variance and covariance ) on top of that such type series exhibit heavier tails i.e. probability of having very large values or very small values tend to be higher relative to normal distribution , so your input is highly appreciated
keep up the good work
But what about conformal predictions? :D
(Vincent here) Noted! There's a long todo list for ideas, but I agree conformal predictions deserve attention.
It would be interesting to compare to Bayesian linear regression.
How does the w parameter map to tge quantile?
how do you go from the free parameter of the pinball loss to the quantile you want to predict?
(Vincent here) I might recommend playing with the notebook to unravel this one. But I'll keep this idea in mind because it might be an interesting appendix short later.