tsfel
Prophet
Our great sponsors
| tsfel | Prophet | |
|---|---|---|
| 1 | 221 | |
| 855 | 17,743 | |
| 6.2% | 1.2% | |
| 7.9 | 6.2 | |
| 26 days ago | 25 days ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | 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.
tsfel
-
Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
For feature extraction check out tsfel:
https://github.com/fraunhoferportugal/tsfel
Prophet
-
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?
tsfresh - Automatic extraction of relevant features from time series:
tensorflow - An Open Source Machine Learning Framework for Everyone
darts - A python library for user-friendly forecasting and anomaly detection on time series.
upgini - Data search & enrichment library for Machine Learning → Easily find and add relevant features to your ML & AI pipeline from hundreds of public and premium external data sources, including open & commercial LLMs
scikit-learn - scikit-learn: machine learning in Python
forecast - Forecasting Functions for Time Series and Linear Models
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
greykite - A flexible, intuitive and fast forecasting library
MLflow - Open source platform for the machine learning lifecycle
Keras - Deep Learning for humans
sktime - A unified framework for machine learning with time series