Machine learning has become a powerful tool in forecasting, offering greater accuracy than traditional human predictions in today’s data-driven world. The capability of machine learning to predict future trends has significant implications for key sectors such as finance, healthcare, and supply chain management. In this study, ARIMA/SARIMA (AutoRegressive Integrated Moving Average/Seasonal AutoRegressive Integrated Moving Average), alongside Prophet, a scalable forecasting tool developed by Facebook based on a generalized additive model, are considered. These models are applied to predict the demand for antidiabetic drugs. The records were collected by the Australian Health Insurance Commission. This dataset was sourced from Medicare Australia. The study evaluates the performance of these models based on their Mean Absolute Error (MAE), a key metric for assessing forecast accuracy. Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) are also considered. The outcome of the comparative analysis shows that the Prophet model outperformed both ARIMA and SARIMA models, achieving an MAE of 0.74, which is significantly lower than the MAE values of 2.18 and 3.02 obtained by SARIMA and ARIMA, respectively. Prophet's superior performance shows its effectiveness in handling complex, non-linear trends and seasonal patterns often observed in real-world time series data. This research contributes to the growing knowledge of machine learning-based forecasting and shows the importance of advanced models like Prophet in optimizing business operations and driving innovation. The findings from this research offer valuable guidance for data experts, analysts, and researchers in selecting the best forecasting methods for reliable predictions.
| Published in | Research & Development (Volume 5, Issue 4) |
| DOI | 10.11648/j.rd.20240504.13 |
| Page(s) | 110-120 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
AutoRegressive Integrated Moving Average (ARIMA), Seasonal AutoRegressive Integrated Moving Average (SARIMA), Mean Absolute Percentage Error (MAPE), Prophet, Time Series Forecasting, Comparative Analysis
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APA Style
Kwarteng, S. B., Andreevich, P. A. (2024). Comparative Analysis of ARIMA, SARIMA and Prophet Model in Forecasting. Research & Development, 5(4), 110-120. https://doi.org/10.11648/j.rd.20240504.13
ACS Style
Kwarteng, S. B.; Andreevich, P. A. Comparative Analysis of ARIMA, SARIMA and Prophet Model in Forecasting. Res. Dev. 2024, 5(4), 110-120. doi: 10.11648/j.rd.20240504.13
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Download@article{10.11648/j.rd.20240504.13,
author = {Samuel Baffoe Kwarteng and Poguda Aleksey Andreevich},
title = {Comparative Analysis of ARIMA, SARIMA and Prophet Model in Forecasting
},
journal = {Research & Development},
volume = {5},
number = {4},
pages = {110-120},
doi = {10.11648/j.rd.20240504.13},
url = {https://doi.org/10.11648/j.rd.20240504.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.rd.20240504.13},
abstract = {Machine learning has become a powerful tool in forecasting, offering greater accuracy than traditional human predictions in today’s data-driven world. The capability of machine learning to predict future trends has significant implications for key sectors such as finance, healthcare, and supply chain management. In this study, ARIMA/SARIMA (AutoRegressive Integrated Moving Average/Seasonal AutoRegressive Integrated Moving Average), alongside Prophet, a scalable forecasting tool developed by Facebook based on a generalized additive model, are considered. These models are applied to predict the demand for antidiabetic drugs. The records were collected by the Australian Health Insurance Commission. This dataset was sourced from Medicare Australia. The study evaluates the performance of these models based on their Mean Absolute Error (MAE), a key metric for assessing forecast accuracy. Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) are also considered. The outcome of the comparative analysis shows that the Prophet model outperformed both ARIMA and SARIMA models, achieving an MAE of 0.74, which is significantly lower than the MAE values of 2.18 and 3.02 obtained by SARIMA and ARIMA, respectively. Prophet's superior performance shows its effectiveness in handling complex, non-linear trends and seasonal patterns often observed in real-world time series data. This research contributes to the growing knowledge of machine learning-based forecasting and shows the importance of advanced models like Prophet in optimizing business operations and driving innovation. The findings from this research offer valuable guidance for data experts, analysts, and researchers in selecting the best forecasting methods for reliable predictions.
},
year = {2024}
}
TY - JOUR T1 - Comparative Analysis of ARIMA, SARIMA and Prophet Model in Forecasting AU - Samuel Baffoe Kwarteng AU - Poguda Aleksey Andreevich Y1 - 2024/10/18 PY - 2024 N1 - https://doi.org/10.11648/j.rd.20240504.13 DO - 10.11648/j.rd.20240504.13 T2 - Research & Development JF - Research & Development JO - Research & Development SP - 110 EP - 120 PB - Science Publishing Group SN - 2994-7057 UR - https://doi.org/10.11648/j.rd.20240504.13 AB - Machine learning has become a powerful tool in forecasting, offering greater accuracy than traditional human predictions in today’s data-driven world. The capability of machine learning to predict future trends has significant implications for key sectors such as finance, healthcare, and supply chain management. In this study, ARIMA/SARIMA (AutoRegressive Integrated Moving Average/Seasonal AutoRegressive Integrated Moving Average), alongside Prophet, a scalable forecasting tool developed by Facebook based on a generalized additive model, are considered. These models are applied to predict the demand for antidiabetic drugs. The records were collected by the Australian Health Insurance Commission. This dataset was sourced from Medicare Australia. The study evaluates the performance of these models based on their Mean Absolute Error (MAE), a key metric for assessing forecast accuracy. Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) are also considered. The outcome of the comparative analysis shows that the Prophet model outperformed both ARIMA and SARIMA models, achieving an MAE of 0.74, which is significantly lower than the MAE values of 2.18 and 3.02 obtained by SARIMA and ARIMA, respectively. Prophet's superior performance shows its effectiveness in handling complex, non-linear trends and seasonal patterns often observed in real-world time series data. This research contributes to the growing knowledge of machine learning-based forecasting and shows the importance of advanced models like Prophet in optimizing business operations and driving innovation. The findings from this research offer valuable guidance for data experts, analysts, and researchers in selecting the best forecasting methods for reliable predictions. VL - 5 IS - 4 ER -
Faculty of Innovation Technology, National Research Tomsk State University, Tomsk State, Russian Federation
Faculty of Innovation Technology, National Research Tomsk State University, Tomsk State, Russian Federation
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