Metadata-Version: 2.4
Name: ete_ts
Version: 0.2.0
Summary: A package for analysing different caractheristics of time series data.
Home-page: https://github.com/franciscovmacieira/easytime.git
Author: Francisco Macieira
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE.md
License-File: NOTICE.md
Requires-Dist: numpy~=1.26.4
Requires-Dist: antropy~=0.1.9
Requires-Dist: pycatch22~=0.4.5
Requires-Dist: tsfel~=0.1.9
Requires-Dist: tsfeatures~=0.4.5
Requires-Dist: statsmodels~=0.14.4
Requires-Dist: ruptures~=1.1.9
Requires-Dist: scikit-learn~=1.6.1
Dynamic: author
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-dist
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# Welcome to "Easy to Explain: Time-Series Features"

This Python library offers diverse solution for advanced time-series analysis. This library is built to empower developers and data scientists by simplifying complex time-series tasks.

To acess the full documentation, visit our official website: https://franciscovmacieira.github.io/easytime/

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## What It Does

`ete_ts` equips you with a robust set of features to master your time-series data:

**Trend Analysis:** Quantify the direction, strength, and stability of the trend in your time-series.

**Noise & Volatility Modeling:** Characterize the randomness, complexity, and predictability of your time-series.

**Seasonality Detection:** Identify and measure the strength of recurring, cyclical patterns.

**Model Selection:** Extract key statistical properties to guide your choice of forecasting models.

**Clustering & Classification:** Generate unique fingerprints for your time-series to use in machine learning tasks.

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## Installation

Get started in seconds.

```bash
pip install ete_ts
```

## Context

This library was developed as the focus of a research initiative by Francisco Macieira, an undergraduate student of Artificial Intelligence and Data Science at FCUP. The project was supervised by Professor Moisés Santos, affiliated with both FCUP and FEUP.
