darts
meta
darts | meta | |
---|---|---|
47 | 139 | |
7,321 | 159 | |
2.2% | 1.3% | |
9.1 | 0.0 | |
1 day ago | 10 days ago | |
Python | Python | |
Apache License 2.0 | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
darts
- Darts: Python lib for forecasting and anomaly detection on time series
-
[D] Doubts on the implementation of LSTMs for timeseries prediction (like including weather forecasts)
Don't use an LSTM. Get up to date with SoTA methods and read the papers in the field. LSTMs are not the way forward. Read the papers I suggested. It would be very useful to come to grips with both the Time Series Repository (https://github.com/thuml/Time-Series-Library) and Darts (https://github.com/unit8co/darts) as these are widely used for research and in industry.
-
Facebook Prophet: library for generating forecasts from any time series data
As others have pointed out, Prophet is not a particularly good model for forecasting, and has been superseded by a multitude of other models.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 experimenting 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).
-
Elevate Your Python Skills: Machine Learning Packages That Transformed My Journey as ML Engineer
3. darts
-
Aeon: A unified framework for machine learning with time series
Looking forward to checking this out! How does this compare with darts[1]?
[1] https://unit8co.github.io/darts/
- [D] Hybrid forecasting framework ARIMA-LSTM
-
[D] Do any of you have experience using Darts for forecasting?
Darts is an open-source Python library by Unit8 for easy handling, pre-processing, and forecasting of time series. It contains an array of models, from standard statistical models such as ARIMA to deep neural networks. https://unit8co.github.io/darts/
-
gluonts VS darts - a user suggested alternative
2 projects | 13 Apr 2023
active support
-
A Simple Guide to Feature Engineering in the Forecast Menu
The new Forecast menu, featuring the open-source Darts Time Series library, offers script-friendly functionality. It's also easy to use. Don't have any data to load yet? Enter through the Stocks or Crypto menus.
-
Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
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)
* Many recent deep learning models (N-BEATS, TFT, etc)
* Seamlessly works on multi-dimensional series
* Models can be trained on multiple series
* 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
* There's even now an explainability module for some of the models - showing you what matters for computing the forecasts
* (coming soon): an 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
meta
- Fluffypony resigns from the Core Team. Thank you for all you did for Monero, Ric!
- Monero Community Crowdfunding System (CCS) Wallet Hacked and Drained
-
Proposal- Remove Light Wallets from sidebar and getmonero.org that don't support switching server URLs
Recommended Light Wallets on the Reddit sidebar and getmonero.org site should follow the Light Wallet API spec (https://github.com/monero-project/meta/blob/master/api/lightwallet_rest.md) and allow users to safely specify their own light wallet server URL in a very similar way that any other recommended wallet allows you to safely specify and use your own Monero node URL, otherwise, I believe these wallets should not be listed, or recommended. And, of course, the wallet should function in the same way with a user-provided URL as it does with the default, proprietary server, so long as the user-provided server follows the Light Wallet API spec.
-
Meeting summary: Monero Research Lab, 1 March 2023
Another big discussion planned for tomorrow, Wednesday, 8 March 2023: https://github.com/monero-project/meta/issues/808
- MoneroKon 2023 Planning Meeting: Saturday 4th March 2023 @ 18:00 UTC
- Removing or restricting tx_extra will be discussed at tomorrow's Monero Research Lab meeting 15 February, 17:00 UTC.
-
A Radical Change in Terminology - Seraphis as Opportunity
A little late to the party; A long time ago I did some brainstorming about this myself:
-
How is Monero audited?
Here you go, audit should be found here
-
The Monero Standard #33: SethForPrivacy on a new Monero podcast, Gupax – a GUI for mining XMR released, MoneroKon funded, and more...
correction. MoneroKon 2023 Planning Meeting is scheduled for Saturday 14th January 2023 @ 18:00 UTC
- MoneroKon 2023 Planning Meeting today @ 18:00 UTC
What are some alternatives?
sktime - A unified framework for machine learning with time series
monero - Monero: the secure, private, untraceable cryptocurrency
pytorch-forecasting - Time series forecasting with PyTorch
polyseed - Mnemonic seed library for Monero and other CryptoNote-based currencies.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
monero-site
Kats - Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.
monero-lws - Monero Light Wallet Server (scans monero viewkeys and implements mymonero API)
tsai - Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
OSPEAD - Optimal Static Parametric Estimation of Arbitrary Distributions (OSPEAD) for the Monero decoy selection algorithm
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
research-lab - A general repo for Monero Research Lab work in progress and completed work