Error In Alignment (ERAL) Algorithm - Revision history

Admino739mjm7 at 09:55, 3 October 2025

2025-10-03T09:55:20Z

← Older revision		Revision as of 09:55, 3 October 2025
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	=== Dataset Information ===		=== Dataset Information ===
	<p style="line-height: 1.5em">		<p style="line-height: 1.5em">
	~~TThe~~ ERAL algorithm is developed for Python. For more information and examples of use see [https://repo.ijs.si/zstrzinar/eral.git here]		The ERAL algorithm is developed for Python. For more information and examples of use see [https://repo.ijs.si/zstrzinar/eral.git here]
	</p>		</p>

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	=== Maturity===		=== Maturity===
	<p style="line-height: 1.5em">		<p style="line-height: 1.5em">
	~~The datasets are fully documented and available~~. </p>		Available. </p>

	=== Licence===		=== Licence===

Admino739mjm7: /* Relevant Categories */

2025-10-03T09:47:38Z

Relevant Categories

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	== Relevant Categories==		== Relevant Categories==
	[[Category:Library]][[Category:Jupyter Notebook]][[Category:Predictive Maintenance]][[Category:Machine Learning]][[Category:~~Quality Control]][[Category:Anomaly Detection~~]]		[[Category:Library]][[Category:Jupyter Notebook]][[Category:Predictive Maintenance]][[Category:Machine Learning]][[Category:Python]]

Admino739mjm7: /* From source = */

2025-10-03T09:45:31Z

From source =

← Older revision		Revision as of 09:45, 3 October 2025
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	</div>		</div>

	=== From source ====		=== From source ===
	To access ERAL source code, please clone the git repository:		To access ERAL source code, please clone the git repository:
	<div align="center" >		<div align="center" >

	{\| class="wikitable" style="margin:auto; width:~~500px~~; line-height: 1.5em"		{\| class="wikitable" style="margin:auto; width:400px; line-height: 1.5em"
	\|-		\|-
	\| '''git clone https://repo.ijs.si/zstrzinar/eral.git'''		\| '''git clone https://repo.ijs.si/zstrzinar/eral.git'''

Admino739mjm7: /* Usage */

2025-10-03T09:45:14Z

Usage

← Older revision		Revision as of 09:45, 3 October 2025
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	<p style="line-height: 1.5em">		<p style="line-height: 1.5em">
	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'		=== 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:
	<div align="center" >		<div align="center" >
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	</div>		</div>

	'From source'		=== From source ====
	To access ERAL source code, please clone the git repository:		To access ERAL source code, please clone the git repository:
	<div align="center" >		<div align="center" >

	{\| class="wikitable" style="margin:auto; width:~~300px~~; line-height: 1.5em"		{\| class="wikitable" style="margin:auto; width:500px; line-height: 1.5em"
	\|-		\|-
	\| '''git clone https://repo.ijs.si/zstrzinar/eral.git'''		\| '''git clone https://repo.ijs.si/zstrzinar/eral.git'''

Admino739mjm7: Created page with "Python-based development of algorithm designed for time series alignment and averaging. Image 1: ''ERAL versus other algorithms'' == 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 e..."

2025-10-03T09:44:17Z

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..."

New page

<strong>Python-based development of algorithm designed for time series alignment and averaging. </strong> [[File:ERAL Trace.png|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 employs a fuzzy clustering-inspired iterative process for temporal alignment and averaging, avoiding the pathological artifacts often introduced by popular time-warping methods.
</p>

== Asset Details ==
=== Dataset Information ===
<p style="line-height: 1.5em">
TThe ERAL algorithm is developed for Python. For more information and examples of use see [https://repo.ijs.si/zstrzinar/eral.git here]
</p>

== Usage ==
<p style="line-height: 1.5em">
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:
<div align="center" >

{| class="wikitable" style="margin:auto; width:300px; line-height: 1.5em"
|-
| '''pip install eral'''
|-
|}

</div>

'From source'
To access ERAL source code, please clone the git repository:
<div align="center" >

{| class="wikitable" style="margin:auto; width:300px; line-height: 1.5em"
|-
| '''git clone https://repo.ijs.si/zstrzinar/eral.git'''
|-
|}

</div>

The repository contains a requirements file, ensure you have all the requirements

<div align="center" >

{| class="wikitable" style="margin:auto; width:300px; line-height: 1.5em"
|-
| '''pip install -r requirements.txt'''
|-
|}

</div>
</p>

=== Maturity===
<p style="line-height: 1.5em">
The datasets are fully documented and available. </p>

=== Licence===
Open source, CC BY 4.0

== Resources ==
<ul>
<li>Implementation available [https://repo.ijs.si/zstrzinar/eral.git here]</li>
<li>Provided by [https://www.ijs.si/ijsw/V001/JSI Jožef Stefan Institute (JSI)]</li>
</ul>

==== Acknowledgement====
<p style="font-size:90%;line-height: 1.5em">
''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.''
</p>

== Relevant Categories==
[[Category:Library]][[Category:Jupyter Notebook]][[Category:Predictive Maintenance]][[Category:Machine Learning]][[Category:Quality Control]][[Category:Anomaly Detection]]