Aeon: A unified framework for machine learning with time series
1–10 of 24 posts
Re: Aeon: A unified framework for machine learning with time series
#2Looking forward to checking this out! How does this compare with darts[1]?
Re: Aeon: A unified framework for machine learning with time series
#3I wonder why aeon split from sktime.
https://twitter.com/sktime_toolbox/status/164721412371161907...
Re: Aeon: A unified framework for machine learning with time series
#4Wondering how it compares to the rest of the lot: sktime, tslearn, darts, pyts, and cesium.
Re: Aeon: A unified framework for machine learning with time series
#5It 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.
Re: Aeon: A unified framework for machine learning with time series
#6It 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.
Prophet has worked so well for us, especially since we have a TON of custom events and holidays to consider. None of the other approaches have really come close.
Re: Aeon: A unified framework for machine learning with time series
#7Wondering how it compares to the rest of the lot: sktime, tslearn, darts, pyts, and cesium.
Re: Aeon: A unified framework for machine learning with time series
#8I wonder why aeon split from sktime. https://twitter.com/sktime_toolbox/status/164721412371161907...
Here is a little about it: https://astrojuanlu.substack.com/p/episodio-70
Re: Aeon: A unified framework for machine learning with time series
#9It 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.
It depends on your time series, really. If the samples are evenly spaced, e.g., your sensor gives you a reading every millisecond, your measured sequences aren’t partially overlapping, and you don’t have structured discrete events, then time isn’t very useful. You can always rescale time so that it is just the same as the index.
For your calendar example, date information is very useful because patterns tend to exhibit a cyclic nature across years and there’s discrete special events (holidays). With enough data, you probably don’t need to include the date, but it’s informative for smaller data sets.
Re: Aeon: A unified framework for machine learning with time series
#10To 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.
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.