Prophet
darts
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Prophet | darts | |
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214 | 42 | |
15,904 | 5,908 | |
1.5% | 5.0% | |
8.5 | 6.9 | |
10 days ago | 7 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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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.
Prophet
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Complete: D214 - MSDA Capstone
My rescue came from discovering some of the alternatives to ARIMA/SARIMA, which was the extent of what we had covered for time series data. A series of searches eventually led me to some automated time series analysis packages, one of which was Prophet, an open source time series package released by Facebook's core data science team. This was a life saver, being a much more efficient and more effective forecasting tool than sloooowly iterating through ARIMA/SARIMA models that seemed to want to fight with me. If you're going to do a time series analysis for your capstone, I strongly suggest taking a look at using Prophet.
- Dec 12, 2022 FLiP Stack Weekly
- Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
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[D] Time Series Question
Prophet
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LSTM/CNN architectures for time series forecasting[Discussion]
Prophet
- Eden
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Predição de ações na bolsa de valores com Python e Facebook Prophet
Prophet: Automação preditiva.
- Time series analysis of Bitcoin price in Python with fbprophet ?!
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Data Science toolset summary from 2021
Prophet - It is a time-series forecasting library built by Facebook. 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. Link - https://github.com/facebook/prophet
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Personal Support at Internet Scale
We run an anomaly detection app powered by Facebook's Prophet forecasting library. It tells us if metrics dip or rise in unexpected ways ("Did signups drop? Is something broken with that flow?"). We built the service because customers kept reaching out to tell us some feature broke before we noticed. Normally these issues show up in product data, so the app looks for these anomalies and tells us when they happen.
darts
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gluonts VS darts - a user suggested alternative
2 projects | 13 Apr 2023
active support
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Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
Darts gives you a lot of options, including newer deep learning approaches like NBEATS and NHiTS.
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.
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).
- [D] Time Series Question
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[D] Fool me once, shame on you; fool me twice, shame on me: Exponential Smoothing vs. Facebook's Neural-Prophet.
There is also a version of N-BEATS in Darts (https://github.com/unit8co/darts) that extends the original N-BEATS by * Accepting exogenous covariate time series * Being able to produce probabilistic forecasts * Working on multivariate time series (all of this out of the box, fit() / predict() style) :D
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DS student here: why use R over Python?
Have you checked out darts in python for time series analysis? https://unit8co.github.io/darts/
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[P] Using transformers for time-series forecasting
N-HiTS, N-BEATS, as well as any sklearn model (or LightGBM, which often performs very well) are all available in Darts, with the same easy & simple API: https://github.com/unit8co/darts
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[D] Recent research and methods for time series forecasting
DARTS https://github.com/unit8co/darts (PyTorch based)
What are some alternatives?
tensorflow - An Open Source Machine Learning Framework for Everyone
sktime - A unified framework for machine learning with time series
pytorch-forecasting - Time series forecasting with PyTorch
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
scikit-learn - scikit-learn: machine learning in Python
greykite - A flexible, intuitive and fast forecasting library
MLflow - Open source platform for the machine learning lifecycle
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.
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
Keras - Deep Learning for humans