I did not try Rocket or something simpler - I specifically wanted to use the opportunity to dig into fastai.
Thanks for the comment about losing resolution - something to keep in mind. In my case, though, airliners change altitude in significant steps - the only changes I were interested in for this problem. Also, a step change takes substantial time relative to a pixel’s resolution.
By inference, I simply meant subjecting the models to unseen test images, i.e. I did not deploy them to production.
Thanks for your reply @oguiza. I actually forgot to add another example: This one also uses the principle of first converting a time-series to an image, but, instead of performing classification, it performs regression. Please see:
Granted, this approach is probably an overkill. Also, it is probably not as accurate as more traditional approaches. But, I was curious about how it would do. This report links to the actual notebook. At the bottom of the notebook I have visualized the regression predictions in green dotted lines: notebook
Not presupposing any particular interest in this, but as I said here, I’m happy to answer any questions anyone might have, help with troubleshooting if you want to try it out, and also just hear any feedback.
Meeting with Angus Dempster - Rocket, MiniRocket, and MultiRocket
Hi all,
I’d like to invite you to participate in a web meeting we’ll have with Angus Dempster next week (@angusde ).
For those of you who don’t know him, Angus is a Ph.D. student at Monash University in Australia (a world-class group in time series research) and is one of the authors of several outstanding papers in Time Series Classification.
@oguiza Does your FastAI based TS library include “tft” implementation, like the one in pytorch ? Do you have a separate dataset/dataloader to handle it ?
I was advised by @tcapelle to share my Pytorch based probabilistic time series framework work here. Kindly have a look at https://github.com/zalandoresearch/pytorch-ts for some state of the art uni and multivariate time series methods that are implemented. Thanks!
Hi @oguiza, hope you are fine.
Can you suggest me some work for stock-price prediction(or crypto-price prediction).Can it be done using tsai?
And, most importantly, do these models(i.e if someone is really benefitting from it), or it is nearly impossible to make accurate stock-price prediction models?
Hi, hope all of you are fine.
Kindly help me with making the TimeSeries Dataset for Crypto Prices Data(including Open,High,Low,Close,Volume,MarketCap).
That’d be great. MiniRocket is a good starting point. You may also take a look at MiniRocketFeaturesPlus and MiniRocketPlus. One of the arguments available is add_lsaz (longest subsequence above zero). This is the feature that scored best in MultiRocket. But there are many other options that can be used with MultiRocket (which makes it more complex too).
Please, feel free to submit a PR with a MultiRocket implementation if you decide to move forward.
First of all, great implementation @oguiza, and credits to the people who have contributed to the project. I appreciated the effort.
I was wondering about the idea behind using None in item transformations such as [None, [TSRegression()]] or [None, [Categorize()]]. I can’t truly remember but I might also have seen without None versions of them.
Hi @hiromi,
It’s great to hear again from you. I’ll always be grateful for the great summaries you created based on Jeremy’s classes
I am not sure if it is not as typical usage for everybody else.
TBH this is a common use case, and it’s true that it’s not well resolved yet. There aren’t many public datasets that contain this type of data (it’s more often used in forecasting - TFT model for example allows multiple types of input data).
A model I’ve used when needed to use categorical and continuous data is TSiTPlus. It’s a variation of ViT adapted to work with time series.
