Documentation: comparison to sktime and tslearn about darts HOT 5 CLOSED

Hi @woj-i - it is a very good point and we'll add a more detailed description in the future.

Long story short - our library was built mostly based on our internal use cases with forecasting in mind as the main priority (at the moment of starting a lib we were not able to find any comprehensive implementation of models in Python) - therefore we brought in a lot of the classical models, added wrappers for the modern, popular ones and implemented some of the neural networks (RNN/TCN) from scratch in pytorch. In the future we plan to add more models that we are going to use internally. We wanted to focus on two things:

1. Readable, immutable Timeseries object to hold time series information.
2. Ease of applying new, sophisticated forecasting models, univariate and multivariate to Timeseries and validating their quality with backtesting.

The rest of the features like preprocessing is delivered on a nice-to-have basis, but the above 2 points are a must and ATM we are still thinking about new models and simplifying backtesting validation.

In terms of sktime, the library seems to provide a very nice API that extends sklearn objects and is meant for more generic-purpose use of time series with quite a broad set of tools to analyze/annotate/preprocess them. At the moment of writing there are also basic forecasting algorithms like ARIMA or 4Theta, although the more complex ones (multivariate models or eg. neural networks) are missing.

I believe the case is similar for the tslearn, library meant for the ML tasks on top of timeseries. Therefore if you want to work with forecasting challenges based on uni-/multivariate timeseries and are happy with a very simple wrapper Timeseries object on top of pandas dataframe, give Darts a shot. If you have different usecases in mind it might be worth looking into sktime or other libraries from their list.

Thank you @mloning for including us on your list - we are super excited to see us listed there! Could you maybe change the description a bit to reflect our goal a bit more? Eg. "Python collection of time series forecasting tools, from preprocessing to models (uni-/multivariate, prophet, neural networks) and backtesting utilities."

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FYI We're keeping a list of related software here. There are a few more packages that offer a scikit-learn-like forecasting API that you may want to compare.

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@TheMP I've changed the description! We're currently working on extending the support for multivariate data. We also have a companion package for common deep learning architecture, at the moment mostly time series classifiers though, check out the dev branch: https://github.com/sktime/sktime-dl/tree/dev

We'd be very happy to collaborate further, I think working towards a more unified ecosystem for time series analysis is crucial to make it easier for people to understand and use the existing time series tools.

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Hi @mloning - many thanks! Totally agree, the time series didn't receive enough attention so far and we're happy to see not only we noticed that problem. They deserve a more unified environment and a proper treatment as first-class citizens :)

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I think working towards a more unified ecosystem for time series analysis is crucial to make it easier for people to understand and use the existing time series tools.

I agree. Having a darts wrapper/extension in sktime would be really nice similar to what is done for statsmodels (Link)!

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