Wondering how it compares to the rest of the lot: sktime, tslearn, darts, pyts, and cesium.
> Wondering how it compares to the rest of the lot: sktime
It's a fork of sktime. Last common commit before the fork is on Jan 30, 2023.
Aeon: A unified framework for machine learning with time series
11–20 of 24 posts
Re: Aeon: A unified framework for machine learning with time series
#12It 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. Prophet, for example uses dates to create Fourier terms and indicators to holidays for example and that just seems like a more sane approach.
Yeah most of the time series data I've had to work with I end up spending a huge amount of time interpolating (so all time slices have some data even if it isn't real) or aggregating to some common denominator (e.g taking sporadic sales and summing up to daily sales). I get why most packages expect nicely spaced or evenly dense data, but boy I would love if I had more options there.
Re: Aeon: A unified framework for machine learning with time series
#13Aeon 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
Re: Aeon: A unified framework for machine learning with time series
#14Looking forward to checking this out! How does this compare with darts[1]? [1] https://unit8co.github.io/darts/
Darts is a commercial FOSS, aeon is community driven. Also aeon is more following scikit-learn.
Re: Aeon: A unified framework for machine learning with time series
#15Re: Aeon: A unified framework for machine learning with time series
#16Aeon 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.
Re: Aeon: A unified framework for machine learning with time series
#17Recently aeon included a new implementation for both PAA and SAX transformations that are much more efficient and much faster !
Re: Aeon: A unified framework for machine learning with time series
#18all of the functionalities of aeon can easily be mastered thanks for the help of its documentation and example notebooks.
Re: Aeon: A unified framework for machine learning with time series
#19I wonder why aeon split from sktime. https://twitter.com/sktime_toolbox/status/164721412371161907...
See my comment above, mostly all active core devs from sktime at that time left or had to leave to the aeon project
Re: Aeon: A unified framework for machine learning with time series
#20hi, 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
https://github.com/aeon-toolkit/aeon/issues/456
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.
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.