awesome_time_series_in_python
github.com/maxbenchrist/awesome_time_series_in_python ↗This curated list contains python packages for time series analysis
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me libraries resources from awesome_time_series_in_python"
Installation instructions →What's inside
Libraries
- Arrow
A sensible, human-friendly approach to creating, manipulating, formatting and converting dates, times, and timestamps
- bta-lib
Technical Analysis library in pandas for backtesting algotrading and quantitative analysis
- Darts
A library making it very easy to produce forecasts using a wide range of models, from ARIMA to deep learning. Also does ensembling, model selection and more.
- ETNA
A python library for time series forecasting and analysis with temporal data structure always in mind. Includes a variety of predictive models with unified interface along with EDA and validation methods
- fecon235
Computational tools for financial economics
- ffn
financial function library
Examples or singular models
- artic
High performance datastore for time series and tick data
- automl_service
Fully automated time series classification pipeline, deployed as a web service
- awesome-public-datasets
This huge list of public datasets also has a section on time series datasets
- cesium
Time series platform with feature extraction aming for non uniformly sampled signals
- Deep learning methods for time series classification
A collection of common deep learning architectures for time series classification
- ecmwf_models
Readers and converters for climate reanalysis data
Showing a sample of 65 resources. View the full list on GitHub →