A library for Time Series exploratory data analysis

timeseries, time-series, profiling, exploratory-data-analysis, time-series-analysis, forecasting, data-science, machine-learning, data-mining, jupyter, data-visualization, eda, exploratory-data-visualizations, pandas, python
pip install tslumen==0.0.1


A library for exploratory analysis of Time Series data

tslumen helps bring to light the key characteristics of your time series data with rich, pre-canned artifacts, packed with charts and statistical information. The primary goal of tslumen is to expedite and bring consistency to how time series EDA is performed, allowing you to uncover the fundamental aspects in seconds rather than hours or days.

Key features

  • Platform agnostic, integrates nicely with your datascience workspace
  • Built on open source technology and research
  • Highly customizable and extensible
  • Data (profiling results) completely detached from the visuals
  • Can be executed from the command line
  • Efficient execution using parallel processing
  • Includes a great number of statistical information, including descriptive statistics statistical tests like KPSS or ADF, correlation, tsfeatures, etc.
  • Various plots specifically tailored to time series analysis
  • Self-contained HTML report that can easily be shared with interested parties
  • Fully interactive dashboard for a richer experience and detailed exploration

See https://hsbc.github.io/tslumen/ for the complete documentation.


From PyPI:

pip install -U tslumen

From source:

# cd into tslumen after cloning the repo
make install


Refer to the Quick Start page of the documentation for a brief tour of the package.

Complete example notebooks can be found on the User Guide section of the documentation.


Contributions to tslumen are welcome. Please see our contribution guide for more details.