TOtosh·Mar 5, 2024Submission
Darts: Python lib for forecasting and anomaly detection on time series
4 points · Open discussionunit8co
darts
Python2021–2024
unit8co / darts
darts
A python library for user-friendly forecasting and anomaly detection on time series.
10Distinct people
12Mentions
0Explicit recommenders
9.6KGitHub stars
What to know before exploring
What it is
A python library for user-friendly forecasting and anomaly detection on time series.
GitHub
Where it is used
No documented product use has been verified yet.
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by year12 dated mentions
Observed mentions: 2021-06-22 07:37:15 UTC to 2024-03-05 09:01:29 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 7 | 2 | 5 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
12 sourcesAs others have pointed out, Prophet is not a particularly good model for forecasting, and has been superseded by a multitude of other models. If you want to do time series forecasting, I'd recommend using Darts: https://github.com/unit8co/darts. Darts implements a wide range of models and is fairly easy to use.
The problem with time series forecasting in general is that they make a lot of assumptions on the shape of your data, and you'll find you're spending a lot of time figuring out mutating your data. For example, they expect that your data comes at a very regular interval. This is fine if it's, say, the data from a weather station. This doesn't work well in clinical settings (imagine a patient admitted into the ER -- there is a burst of data, followed by no data).
That said, there's some interesting stuff out there that I've been expe
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rimenting with that seems to be more tolerant of irregular time series and can be quite useful. If you're interested in exchanging ideas, drop me a line (email in my profile).
Links for those who are curious:
https://github.com/sktime/sktime
https://github.com/tslearn-team/tslearn
https://github.com/unit8co/darts
https://github.com/johannfaouzi/pyts
https://github.com/cesium-ml/cesium
Also:
https://github.com/timeseriesAI/tsai
In Aeon: A unified framework for machine learning with time seriesI 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)
* Many recent deep learning models (N-BEATS, TFT, etc)
* Seamlessly works on multi-dimensional series
* Models can be trained on multiple series
* Several models support taking in external data (covariates), known either in the past only, or also in the future
* Many models offer rich support for probabilistic forecasts
* Model evaluation is easy: Darts has many metrics, offers backtest etc
* Deep learning scales to large datasets, using GPUs, TPUs, etc
* You can do reconciliation of forecasts at different hierarchical levels
* There's even now an explainability module for some of the models - showing you what matters for computing the forecasts
* (coming soon): a
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n anomaly detection module :)
* (also, it even include FB Prophet if you really want to use it)
Warning: I'm probably biased because I'm Darts creator.
[1] https://github.com/unit8co/darts
To be fair, Darts looks pretty good relative to forecast: https://github.com/unit8co/darts
I would generally prefer R for this kind of stuff as the experts generally write the code, but Darts seems OK and is well-tested, at the very least (haven't had a chance to use it in anger yet).
In Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?Great, another open source tool purporting to solve time series analysis in an "automated way" that my manager will link me tomorrow and ask me to review (as an aside, attempting automated statistics of any kind is incredibly dangerous and misguided, but especially so for time series).
Why should I use this over Darts[1] or just Statsmodels[2], if I need more lower level access and diagnostics? Both of these are far more established.
I dislike that Facebook Prophet was chosen as a benchmark; it's not a difficult benchmark to beat for the majority of time series use cases. It signifies to me that this project might targeting cargo cult data science. Prophet is not particularly good at non-daily timeseries and non-seasonal timeseries. The paper itself admits this[3]. Moreover, it's just a generalized additive model that incorporates holiday
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s.
I don't intend to sound demeaning here, really. But I'm trying to understand what the point is. This doesn't look like someone's weekend project, but we already have plenty of established projects which tackle this effectively.
There are three major markets for time series work:
1. You're an analyst with a lot of domain knowledge who needs to analyze daily, seasonal data but you don't have a strong statistical or engineering background. This person should probably just choose Prophet (again, the developers of Prophet explicitly acknowledge that it's designed for scalable good enough models by non-stats people, not for the best model given the data).
2. You're a data scientist with a good statistical background and you need to produce forecasts. You can afford to dig into what the model is doing and select a model based on a series of diagnostics and knowledge about the data itself. This person should probably choose a more complete suite, like Darts. The important thing here is developing good models quickly while being able to do more than just press a button.
3. You're a data scientist (or statistician) which a very strong statistical background who needs to produce the best model they can for answering a specific question. This person is probably going to use R, Stan, Statsmodels or PyMC to come up with something bespoke. They may or may not need to systematize it, but they don't need to produce quantity over quality.
How does this thing improve the state of the art for any of these markets?
--
1 https://github.com/unit8co/darts
2 https://www.statsmodels.org/stable/index.html
3 https://peerj.com/preprints/3190/
What do you mean by "optional"?. You are including it [2] [3] [4] as one of the options (not re-implementation of the original paper [1]) and it is one of the dependencies [4] (Prophet [5] is an open-source library built by facebook research).
I think we should distinguish between science/open-source and policies when we mention open-source projects.
*References*:
[1]: https://facebook.github.io/prophet/#:~:text=Prophet%20is%20a....
[2]: https://github.com/unit8co/darts/blob/master/darts/models/pr...
[3]: https://github.com/unit8co/darts/blob/master/setup.py#L12
[4]: https://github.com/unit8co/darts/blob/master/requirements/pr...
[5]: https://github.com/facebook/prophet
In Darts: A Python library for easy manipulation and forecasting of time seriesDarts: A Python library for easy manipulation and forecasting of time series
239 points · Open discussionIf you are interested in time series predictions, I would suggest you had a look at darts (https://github.com/unit8co/darts). It's a well designed library which provides a unified API to deal with time series and try/compare different algorithms/frameworks like Prophet, recurrent NNs, etc.
In Is Facebook's “Prophet” the time-series Messiah or just a naughty boy?Darts – Python time series forecasting library
4 points · Open discussionFor training forecasting models on multiple time series (and potentially large datasets), you can take a look at Darts [1] and the blog post [2].
[1]: https://github.com/unit8co/darts/
[2]: https://medium.com/unit8-machine-learning-publication/traini...
In Kats: One stop shop for time series analysis in PythonFor those interested in time series library, we are developing Darts [1], which focuses on making it easy & straightforward to build and use forecasting models. Out of the box it contains traditional models (such as ARIMA) as well as recent deep learning ones (like N-Beats). It also allows to easily train models on multiple time series (potentially scaling to large datasets), as well as on multivariate series (i.e., series made of multiple dimensions). It will soon support probabilistic forecasts as well.
[1] https://github.com/unit8co/darts/
In Kats: One stop shop for time series analysis in Python