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Review of ML and AutoML Solutions to Forecast Time-Series Data

Time-series forecasting is a significant discipline of data modeling where past observations of the same variable are analyzed to predict the future values of the time series. Its prominence lies in different use cases where it is required, including economic, weather, stock price, business developm...

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Detalles Bibliográficos
Autores principales: Alsharef, Ahmad, Aggarwal, Karan, Sonia, Kumar, Manoj, Mishra, Ashutosh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9159649/
https://www.ncbi.nlm.nih.gov/pubmed/35669518
http://dx.doi.org/10.1007/s11831-022-09765-0
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author Alsharef, Ahmad
Aggarwal, Karan
Sonia
Kumar, Manoj
Mishra, Ashutosh
author_facet Alsharef, Ahmad
Aggarwal, Karan
Sonia
Kumar, Manoj
Mishra, Ashutosh
author_sort Alsharef, Ahmad
collection PubMed
description Time-series forecasting is a significant discipline of data modeling where past observations of the same variable are analyzed to predict the future values of the time series. Its prominence lies in different use cases where it is required, including economic, weather, stock price, business development, and other use cases. In this work, a review was conducted on the methods of analyzing time series starting from the traditional linear modeling techniques until the automated machine learning (AutoML) frameworks, including deep learning models. The objective of this review article is to support identifying the time-series forecasting challenge and the different techniques to meet the challenge. This work can be additionally an assist and a reference for researchers and industries demanding to use AutoML to solve the problem of forecasting. It identifies the gaps of the previous works and techniques used to solve the problem of forecasting time series.
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spelling pubmed-91596492022-06-02 Review of ML and AutoML Solutions to Forecast Time-Series Data Alsharef, Ahmad Aggarwal, Karan Sonia Kumar, Manoj Mishra, Ashutosh Arch Comput Methods Eng Original Paper Time-series forecasting is a significant discipline of data modeling where past observations of the same variable are analyzed to predict the future values of the time series. Its prominence lies in different use cases where it is required, including economic, weather, stock price, business development, and other use cases. In this work, a review was conducted on the methods of analyzing time series starting from the traditional linear modeling techniques until the automated machine learning (AutoML) frameworks, including deep learning models. The objective of this review article is to support identifying the time-series forecasting challenge and the different techniques to meet the challenge. This work can be additionally an assist and a reference for researchers and industries demanding to use AutoML to solve the problem of forecasting. It identifies the gaps of the previous works and techniques used to solve the problem of forecasting time series. Springer Netherlands 2022-06-01 2022 /pmc/articles/PMC9159649/ /pubmed/35669518 http://dx.doi.org/10.1007/s11831-022-09765-0 Text en © The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Paper
Alsharef, Ahmad
Aggarwal, Karan
Sonia
Kumar, Manoj
Mishra, Ashutosh
Review of ML and AutoML Solutions to Forecast Time-Series Data
title Review of ML and AutoML Solutions to Forecast Time-Series Data
title_full Review of ML and AutoML Solutions to Forecast Time-Series Data
title_fullStr Review of ML and AutoML Solutions to Forecast Time-Series Data
title_full_unstemmed Review of ML and AutoML Solutions to Forecast Time-Series Data
title_short Review of ML and AutoML Solutions to Forecast Time-Series Data
title_sort review of ml and automl solutions to forecast time-series data
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9159649/
https://www.ncbi.nlm.nih.gov/pubmed/35669518
http://dx.doi.org/10.1007/s11831-022-09765-0
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