- Published
- 2023-07-30
- Venue
- Future Internet, vol. 15, no. 8, pp. 255
- Cited by
- 433
- Open access
- Yes, gold
- DOI
- 10.3390/fi15080255
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.
Cite this paper
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- APA 7
Kontopoulou, V. I., Panagopoulos, A. D., Kakkos, I., & Matsopoulos, G. K. (2023). A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks. Future Internet, 15(8), 255. https://doi.org/10.3390/fi15080255
- MLA 9
Kontopoulou, Vaia I., et al. "A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks." Future Internet, vol. 15, no. 8, 2023, pp. 255, https://doi.org/10.3390/fi15080255.
- Chicago
Kontopoulou, Vaia I., Athanasios D. Panagopoulos, Iοannis Kakkos, and George K. Matsopoulos. "A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks." Future Internet 15, no. 8 (2023): 255. https://doi.org/10.3390/fi15080255.
- GOST
Kontopoulou V. I., Panagopoulos A. D., Kakkos I. [и др.] A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks // Future Internet. — 2023. — Vol. 15, № 8. — P. 255. — URL: https://doi.org/10.3390/fi15080255
- BibTeX
@article{kontopoulou2023review, title = {{A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks}}, author = {Vaia I. Kontopoulou and Athanasios D. Panagopoulos and Iοannis Kakkos and George K. Matsopoulos}, journal = {Future Internet}, year = {2023}, volume = {15}, number = {8}, pages = {255}, doi = {10.3390/fi15080255}, url = {https://doi.org/10.3390/fi15080255} }- RIS
TY - JOUR AU - Kontopoulou, Vaia I. AU - Panagopoulos, Athanasios D. AU - Kakkos, Iοannis AU - Matsopoulos, George K. TI - A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks JO - Future Internet VL - 15 IS - 8 SP - 255 PY - 2023 DA - 2023/07/30 DO - 10.3390/fi15080255 UR - https://doi.org/10.3390/fi15080255 ER -
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