A Literature Review on Some Trends in Artificial Neural Networks for Modeling and Simulation with Time Series
This paper reviews the application of artificial neural network (ANN) models to time series prediction tasks. We begin by briefly introducing some basic concepts and terms related to time series analysis, and by outlining some of the most popular ANN architectures considered in the literature for ti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/17/2/76 |
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| author | Angel E. Muñoz-Zavala Jorge E. Macías-Díaz Daniel Alba-Cuéllar José A. Guerrero-Díaz-de-León |
| author_facet | Angel E. Muñoz-Zavala Jorge E. Macías-Díaz Daniel Alba-Cuéllar José A. Guerrero-Díaz-de-León |
| author_sort | Angel E. Muñoz-Zavala |
| collection | DOAJ |
| description | This paper reviews the application of artificial neural network (ANN) models to time series prediction tasks. We begin by briefly introducing some basic concepts and terms related to time series analysis, and by outlining some of the most popular ANN architectures considered in the literature for time series forecasting purposes: feedforward neural networks, radial basis function networks, recurrent neural networks, and self-organizing maps. We analyze the strengths and weaknesses of these architectures in the context of time series modeling. We then summarize some recent time series ANN modeling applications found in the literature, focusing mainly on the previously outlined architectures. In our opinion, these summarized techniques constitute a representative sample of the research and development efforts made in this field. We aim to provide the general reader with a good perspective on how ANNs have been employed for time series modeling and forecasting tasks. Finally, we comment on possible new research directions in this area. |
| first_indexed | 2024-03-07T22:45:58Z |
| format | Article |
| id | doaj.art-c208b98f75ee417e90ef1b1e32947dbf |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-07T22:45:58Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-c208b98f75ee417e90ef1b1e32947dbf2024-02-23T15:04:30ZengMDPI AGAlgorithms1999-48932024-02-011727610.3390/a17020076A Literature Review on Some Trends in Artificial Neural Networks for Modeling and Simulation with Time SeriesAngel E. Muñoz-Zavala0Jorge E. Macías-Díaz1Daniel Alba-Cuéllar2José A. Guerrero-Díaz-de-León3Departamento de Estadística, Universidad Autónoma de Aguascalientes, Aguascalientes 20100, MexicoDepartment of Mathematics and Didactics of Mathematics, Tallinn University, 10120 Tallinn, EstoniaInstituto Nacional de Estadística y Geografía, Aguascalientes 20276, MexicoDepartamento de Estadística, Universidad Autónoma de Aguascalientes, Aguascalientes 20100, MexicoThis paper reviews the application of artificial neural network (ANN) models to time series prediction tasks. We begin by briefly introducing some basic concepts and terms related to time series analysis, and by outlining some of the most popular ANN architectures considered in the literature for time series forecasting purposes: feedforward neural networks, radial basis function networks, recurrent neural networks, and self-organizing maps. We analyze the strengths and weaknesses of these architectures in the context of time series modeling. We then summarize some recent time series ANN modeling applications found in the literature, focusing mainly on the previously outlined architectures. In our opinion, these summarized techniques constitute a representative sample of the research and development efforts made in this field. We aim to provide the general reader with a good perspective on how ANNs have been employed for time series modeling and forecasting tasks. Finally, we comment on possible new research directions in this area.https://www.mdpi.com/1999-4893/17/2/76time series forecastingartificial neural network architecturesmachine learningdynamical systemstime series statistical modeling techniques |
| spellingShingle | Angel E. Muñoz-Zavala Jorge E. Macías-Díaz Daniel Alba-Cuéllar José A. Guerrero-Díaz-de-León A Literature Review on Some Trends in Artificial Neural Networks for Modeling and Simulation with Time Series Algorithms time series forecasting artificial neural network architectures machine learning dynamical systems time series statistical modeling techniques |
| title | A Literature Review on Some Trends in Artificial Neural Networks for Modeling and Simulation with Time Series |
| title_full | A Literature Review on Some Trends in Artificial Neural Networks for Modeling and Simulation with Time Series |
| title_fullStr | A Literature Review on Some Trends in Artificial Neural Networks for Modeling and Simulation with Time Series |
| title_full_unstemmed | A Literature Review on Some Trends in Artificial Neural Networks for Modeling and Simulation with Time Series |
| title_short | A Literature Review on Some Trends in Artificial Neural Networks for Modeling and Simulation with Time Series |
| title_sort | literature review on some trends in artificial neural networks for modeling and simulation with time series |
| topic | time series forecasting artificial neural network architectures machine learning dynamical systems time series statistical modeling techniques |
| url | https://www.mdpi.com/1999-4893/17/2/76 |
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