It strikes me as a bit weird that these time series packages tend to discard the time component of the data and just.. not do anything with it.<p>Prophet, for example uses dates to create Fourier terms and indicators to holidays for example and that just seems like a more sane approach.
hi, I dont want to enter a public discussion about the split of sktime, I fear the application of Godwin's law. A summary of the key points behind the split from my perspective are here
<a href="https://github.com/aeon-toolkit/aeon/issues/456">https://github.com/aeon-toolkit/aeon/issues/456</a>
the other sides view will no doubt be forthcoming. If you want to chat about it, join our slack and message me, I'm more than happy to help. How are we different? Well I think we can all live together, its open source, but from my perspective the priorities are
1. Align as closely as we can with sklearn, so as to make it completely intuitive how to use aeon if you know sklearn.
2. Focus on implementations of state of the art algorithms for time series machine learners and less on just wrapping other code. The goal is to reduce the lead time from publication of new ideas to widespread adoption
3. Documentation: make it good.<p>my interests primarily lie in classification, clustering and regression, but next year we are going into the forecasting world, plenty of exciting collaborations in the brew.
Aeon is an sktime fork which happened after one of the sktime core developers (Franz K.) took hostage of the sktime project by kicking out other core devs from the GitHub. Its info you can collect from some GH issues
Looking forward to checking this out! How does this compare with darts[1]?<p>[1] <a href="https://unit8co.github.io/darts/" rel="nofollow noreferrer">https://unit8co.github.io/darts/</a>
Aeon has the advantage of including a friendly deep learning framework, all of the models discussed on the 'Deep Learning for Time Series Classification: a review" are included in aeon with the variety of choices on how to change the parameters of the architecture. More state of the art models such as InceptionTime are also included, not only for classification but regression as well and soon forecasting and clustering.
I wonder why aeon split from sktime.<p><a href="https://twitter.com/sktime_toolbox/status/1647214123711619072?cxt=HHwWgICzjZWhitwtAAAA" rel="nofollow noreferrer">https://twitter.com/sktime_toolbox/status/164721412371161907...</a>
To obfuscate the choice of algorithm behind kwargs (as opposed to creating separate classes) has always seemed to me a suspect choice, in sklearn as well as here. And it seems to make development of the package more complex at the expense of... less readable code for the user, with less flexibility for differences in hyperparameter specifications, etc.<p>There are of course exceptions, something like `TrendPredictor(order=1, interp="polynomial")` as an example can be flexibly adapted up or down the hierarchy of model complexity much easier than commenting out different lines.