Error In Alignment (ERAL) Algorithm: Difference between revisions
Created page with "<strong>Python-based development of algorithm designed for time series alignment and averaging. </strong> thumb|right|Image 1: ''ERAL versus other algorithms'' == Asset Description == <p style="line-height: 1.5em"> The ERAL (Error In Alignment) algorithm is a state-of-the-art method designed for time series alignment and averaging. The method obtains the average time series (the prototype) from a set of time series (a class). The developed ERAL e..." |
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=== Dataset Information === | === Dataset Information === | ||
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The ERAL algorithm is developed for Python. For more information and examples of use see [https://repo.ijs.si/zstrzinar/eral.git here] | |||
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The developed algorithm can be installed either as pip package or loaded from gitlab source. | The developed algorithm can be installed either as pip package or loaded from gitlab source. | ||
=== pip package === | |||
To use ERAL, please install the package from pypi.org using pip: | To use ERAL, please install the package from pypi.org using pip: | ||
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=== From source === | |||
To access ERAL source code, please clone the git repository: | To access ERAL source code, please clone the git repository: | ||
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| '''git clone https://repo.ijs.si/zstrzinar/eral.git''' | | '''git clone https://repo.ijs.si/zstrzinar/eral.git''' | ||
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=== Maturity=== | === Maturity=== | ||
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Available. </p> | |||
=== Licence=== | === Licence=== | ||
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== Relevant Categories== | == Relevant Categories== | ||
[[Category:Library]][[Category:Jupyter Notebook]][[Category:Predictive Maintenance]][[Category:Machine Learning]][[Category: | [[Category:Library]][[Category:Jupyter Notebook]][[Category:Predictive Maintenance]][[Category:Machine Learning]][[Category:Python]] | ||
Latest revision as of 09:55, 3 October 2025
Python-based development of algorithm designed for time series alignment and averaging.
Asset Description
The ERAL (Error In Alignment) algorithm is a state-of-the-art method designed for time series alignment and averaging. The method obtains the average time series (the prototype) from a set of time series (a class). The developed ERAL employs a fuzzy clustering-inspired iterative process for temporal alignment and averaging, avoiding the pathological artifacts often introduced by popular time-warping methods.
Asset Details
Dataset Information
The ERAL algorithm is developed for Python. For more information and examples of use see here
Usage
The developed algorithm can be installed either as pip package or loaded from gitlab source.
pip package
To use ERAL, please install the package from pypi.org using pip:
| pip install eral |
From source
To access ERAL source code, please clone the git repository:
| git clone https://repo.ijs.si/zstrzinar/eral.git |
The repository contains a requirements file, ensure you have all the requirements
| pip install -r requirements.txt |
Maturity
Available.
Licence
Open source, CC BY 4.0
Resources
- Implementation available here
- Provided by Jožef Stefan Institute (JSI)
Acknowledgement
This work was funded partly by AI REDGIO 5.0 (101092069) and partly by the Slovenian Research and Innovation Agency (L2-4454, P2-0001). The dataset will be used in the AI REDGIO 5.0 Didactic Factory Pilot DFIII: Self-evolving monitoring systems for assembly production lines.