Viewing profile — hrzn

A few years back I made a modest attempt at writing a concise yet readable sudoku solver in Python - in about 29 lines: https://github.com/hrzn/sudoku/blob/master/sudoku.py Could h…

We likely overestimate AI's short-term impact, and there might even be a financial bubble about to pop. But I also think we underestimate the long-term impact. We're building absol…

> Python had nothing comparable This is in part why we built Darts. Now I think we can say the situation is quite different. Darts offers many things offered by the R forecast pack…

I would recommend Darts in Python [1]. It's easy to use (think fit()/predict()) and includes * Statistical models (ETS, (V)ARIMA(X), etc) * ML models (sklearn models, LGBM, etc) * …

Note that the actual EV of a Cauchy random variable is undefined...

You can install darts with conda using conda install u8darts-all We are working on improving our support of conda based installs.

You can try installing it using pip install 'u8darts[torch]' This will give you everything besides Prophet and pmdarima.

I would say that compared to Greykite, Darts really attempts to unify a wide variety of forecasting models under a common simple and user-friendly API. There are many differences, …

In short: yes, at least anomaly detection. That's one of the main things on our to-do list.

Oh, nice, I didn't know that. I've edited my reply, thanks!

We do have ensemble models. Something like model = NaiveEnsembleModel([model1, model2, ...]) model.fit(my_series) prediction = model.predict() Will return an average prediction. Lo…

Thanks for the feedback, I absolutely agree about the need for easy-to-use tools for dealing with time series. This is exactly the motivation that prompted us to work on Darts init…

> Or is it just turning every column in my pandas dataframe into a series to pass into the covariates array? Basically if you have a multivariate series represented as a pandas dat…

You're welcome, glad you like it and thanks for the feedback :)

In some cases Darts is wrapping around existing models (like Prophet, or statsmodels-based models for instance); in other cases we wrote our own implementations, so it's really a m…

Hi! I'm one core developer (and creator) of the library. Thanks for all the comments. I just wanted to highlight a couple of things that we think are quite cool about Darts: * It m…

This is supported but only by neural-nets models, which are fit using SGD, hence naturally not requiring the whole dataset in memory. Other models like ARIMA do need the full serie…