A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks

Vaia I. Kontopoulou, Athanasios D. Panagopoulos (), Ioannis Kakkos and George K. Matsopoulos
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Vaia I. Kontopoulou: Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
Athanasios D. Panagopoulos: School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
Ioannis Kakkos: Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
George K. Matsopoulos: Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece

Future Internet, 2023, vol. 15, issue 8, 1-31

Abstract: In the broad scientific field of time series forecasting, the ARIMA models and their variants have been widely applied for half a century now due to their mathematical simplicity and flexibility in application. However, with the recent advances in the development and efficient deployment of artificial intelligence models and techniques, the view is rapidly changing, with a shift towards machine and deep learning approaches becoming apparent, even without a complete evaluation of the superiority of the new approach over the classic statistical algorithms. Our work constitutes an extensive review of the published scientific literature regarding the comparison of ARIMA and machine learning algorithms applied to time series forecasting problems, as well as the combination of these two approaches in hybrid statistical-AI models in a wide variety of data applications (finance, health, weather, utilities, and network traffic prediction). Our review has shown that the AI algorithms display better prediction performance in most applications, with a few notable exceptions analyzed in our Discussion and Conclusions sections, while the hybrid statistical-AI models steadily outperform their individual parts, utilizing the best algorithmic features of both worlds.

Keywords: ARIMA; machine learning; deep learning; hybrid; networks; finance; health; weather; MSE; RMSE; MAE; MAPE (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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