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

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 artifici...

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Bibliographic Details
Published in:Future internet Vol. 15; no. 8; p. 255
Main Authors: Kontopoulou, Vaia I., Panagopoulos, Athanasios D., Kakkos, Ioannis, Matsopoulos, George K.
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.08.2023
Subjects:
Accuracy
Algorithms
ARIMA
Artificial intelligence
Autoregressive moving-average models
Communications traffic
Comparative analysis
Data collection
Data mining
Deep learning
finance
Financial services
Forecasting
Forecasting techniques
hybrid
Internet
Machine learning
networks
Statistical analysis
Statistical methods
Time series
China
ISSN:1999-5903, 1999-5903
Online Access:Get full text
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Summary: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.
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ISSN:1999-5903
1999-5903
DOI:10.3390/fi15080255