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darts | Prophet | |
---|---|---|
47 | 221 | |
7,272 | 17,743 | |
2.6% | 1.2% | |
9.1 | 6.2 | |
about 18 hours ago | 22 days ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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
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[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.
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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As others have pointed out, Prophet is not a particularly good model for forecasting, and has been superseded by a multitude of other models.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).
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Elevate Your Python Skills: Machine Learning Packages That Transformed My Journey as ML Engineer
3. darts
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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
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[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/
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gluonts VS darts - a user suggested alternative
2 projects | 13 Apr 2023
active support
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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.
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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
Prophet
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Moirai: A Time Series Foundation Model for Universal Forecasting
https://facebook.github.io/prophet/
"Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well."
- prophet: NEW Data - star count:17116.0
- prophet: NEW Data - star count:17082.0
- Facebook Prophet: library for generating forecasts from any time series data
- prophet: NEW Data - star count:16196.0
- prophet: NEW Data - star count:15889.0
What are some alternatives?
sktime - A unified framework for machine learning with time series
tensorflow - An Open Source Machine Learning Framework for Everyone
pytorch-forecasting - Time series forecasting with PyTorch
scikit-learn - scikit-learn: machine learning in Python
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.
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
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
greykite - A flexible, intuitive and fast forecasting library
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
MLflow - Open source platform for the machine learning lifecycle
nixtla - Python SDK for TimeGPT, a foundational time series model
Keras - Deep Learning for humans