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Establishing diversity in synthetic time series for prediction performance evaluation

Fuente: Zenodo

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Bibliographische Detailangaben
Hauptverfasser:	Bahrpeyma Fouad, Roantree Mark, Cappellari Paolo, Scriney Michael, McCarren Andrew
Format:	Recurso digital
Veröffentlicht:	Zenodo 2021
Schlagworte:	Diversity Time series Forecasting Diverse time series Time series generation Synthetic data Synthetic time series Time series prediction performance evaluation
Online-Zugang:	Acceder al recurso
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author	Bahrpeyma Fouad Roantree Mark Cappellari Paolo Scriney Michael McCarren Andrew
author_facet	Bahrpeyma Fouad Roantree Mark Cappellari Paolo Scriney Michael McCarren Andrew
contents	<p>This dataset enables practitioners to evaluate their time series prediction algorithms on various types of time series</p>
format	Recurso digital
id	zenodo_https___doi_org_10_5281_zenodo_4455631
institution	Zenodo
language
publishDate	2021
publisher	Zenodo
record_format	zenodo
spellingShingle	Establishing diversity in synthetic time series for prediction performance evaluation Bahrpeyma Fouad Roantree Mark Cappellari Paolo Scriney Michael McCarren Andrew Diversity Time series Forecasting Diverse time series Time series generation Synthetic data Synthetic time series Time series prediction performance evaluation <p>This dataset enables practitioners to evaluate their time series prediction algorithms on various types of time series</p>
title	Establishing diversity in synthetic time series for prediction performance evaluation
topic	Diversity Time series Forecasting Diverse time series Time series generation Synthetic data Synthetic time series Time series prediction performance evaluation
url	https://doi.org/10.5281/zenodo.4455631