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Machine Learning Technique in Time Series Prediction of Gross Domestic Product

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Georgios N. KouziokasAuthors Info & Claims

PCI '17: Proceedings of the 21st Pan-Hellenic Conference on Informatics

Article No.: 7, Pages 1 - 2

https://doi.org/10.1145/3139367.3139443

Published: 28 September 2017 Publication History

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Abstract

Artificial intelligence is gaining ground the last years in many scientific sectors with the development of new machine learning techniques. In this research, a machine learning methodology is proposed in the Gross Domestic Product (GDP) time series forecasting. Artificial Neural Networks are implemented in order to develop forecasting models for predicting the Gross Domestic Product. A Feedforward Multilayer Perceptron (FFMLP) was implemented since it is considered as the most suitable in times series forecasting. In order to develop the optimal forecasting model, several network topologies were examined by testing different transfer functions and also different number of neurons in the hidden layers. The results have shown a very precise prediction accuracy regarding the levels of Gross Domestic Product. The proposed technique based on machine learning can be very helpful in public and financial management.

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Ouaadi IIbourk A(2023)The Contribution of Deep Learning Models: Application of LSTM to Predict the Moroccan GDP Growth Using Drought IndexesInternational Conference on Advanced Intelligent Systems for Sustainable Development10.1007/978-3-031-26384-2_25(284-294)Online publication date: 10-Jun-2023
https://doi.org/10.1007/978-3-031-26384-2_25
Ouaadi IIbourk A(2022)Can LSTM Model Predict the Moroccan GDP Growth Using Health Expenditure Features?Proceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems10.1007/978-3-031-20429-6_61(680-689)Online publication date: 13-Dec-2022
https://doi.org/10.1007/978-3-031-20429-6_61
Kouziokas G(2019)Long Short-Term Memory (LSTM) Deep Neural Networks in Energy Appliances Prediction2019 Panhellenic Conference on Electronics & Telecommunications (PACET)10.1109/PACET48583.2019.8956252(1-5)Online publication date: Nov-2019
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Published In

PCI '17: Proceedings of the 21st Pan-Hellenic Conference on Informatics

September 2017

322 pages

ISBN:9781450353557

DOI:10.1145/3139367

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Greek Com Soc: Greek Computer Society
University of Thessaly: University of Thessaly, Volos, Greece

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 September 2017

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Short-paper
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Refereed limited

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PCI 2017

PCI 2017: 21st PAN-HELLENIC CONFERENCE ON INFORMATICS

September 28 - 30, 2017

Larissa, Greece

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Overall Acceptance Rate 190 of 390 submissions, 49%

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Cited By

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Ouaadi IIbourk A(2023)The Contribution of Deep Learning Models: Application of LSTM to Predict the Moroccan GDP Growth Using Drought IndexesInternational Conference on Advanced Intelligent Systems for Sustainable Development10.1007/978-3-031-26384-2_25(284-294)Online publication date: 10-Jun-2023
https://doi.org/10.1007/978-3-031-26384-2_25
Ouaadi IIbourk A(2022)Can LSTM Model Predict the Moroccan GDP Growth Using Health Expenditure Features?Proceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems10.1007/978-3-031-20429-6_61(680-689)Online publication date: 13-Dec-2022
https://doi.org/10.1007/978-3-031-20429-6_61
Kouziokas G(2019)Long Short-Term Memory (LSTM) Deep Neural Networks in Energy Appliances Prediction2019 Panhellenic Conference on Electronics & Telecommunications (PACET)10.1109/PACET48583.2019.8956252(1-5)Online publication date: Nov-2019
https://doi.org/10.1109/PACET48583.2019.8956252
Kouziokas GChatzigeorgiou APerakis K(2018)Multilayer Feed Forward Models in Groundwater Level Forecasting Using Meteorological Data in Public ManagementWater Resources Management10.1007/s11269-018-2126-y32:15(5041-5052)Online publication date: 13-Nov-2018
https://doi.org/10.1007/s11269-018-2126-y
Kouziokas G(2018)Unemployment Prediction in UK by Using a Feedforward Multilayer PerceptronOperational Research in the Digital Era – ICT Challenges10.1007/978-3-319-95666-4_5(65-74)Online publication date: 28-Sep-2018
https://doi.org/10.1007/978-3-319-95666-4_5
Kouziokas GChatzigeorgiou APerakis K(2018)Final Energy Consumption Forecasting by Applying Artificial Intelligence ModelsOperational Research in the Digital Era – ICT Challenges10.1007/978-3-319-95666-4_1(1-10)Online publication date: 28-Sep-2018
https://doi.org/10.1007/978-3-319-95666-4_1

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