Predicting Future Birth Rates with the Use of an Adaptive Machine Learning Algorithm: A Forecasting Experiment for Scotland
Abstract
:1. Introduction
2. Methodology
2.1. Machine Learning Models
Facebook Prophet
2.2. Random Forest
2.3. Extra Trees
2.4. Extreme Gradient Boosting (XGBoost)
2.5. Evaluation Metrics
3. Data
4. Results
5. Concluding Remarks
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Mean | 8.381 |
---|---|
Median | 8.427 |
Maximum | 9.131 |
Minimum | 2.944 |
Standard Deviation | 0.429 |
Skewness | −10.526 |
Kurtosis | 124.054 |
Jarque–Bera | 188,717 *** |
Jarque–Bera probability | [0.000] |
Model | MAE | RMSE | SMAPE |
---|---|---|---|
ARIMA | 0.44 | 0.52 | 0.72 |
Prophet | 0.37 | 0.46 * | 0.54 |
Random Forest | 0.34 | 0.44 * | 0.57 |
Extreme Gradient Boosting | 0.32 | 0.41 * | 0.54 |
Linear Regression | 0.45 | 0.62 | 0.67 |
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Tzitiridou-Chatzopoulou, M.; Zournatzidou, G.; Kourakos, M. Predicting Future Birth Rates with the Use of an Adaptive Machine Learning Algorithm: A Forecasting Experiment for Scotland. Int. J. Environ. Res. Public Health 2024, 21, 841. https://doi.org/10.3390/ijerph21070841
Tzitiridou-Chatzopoulou M, Zournatzidou G, Kourakos M. Predicting Future Birth Rates with the Use of an Adaptive Machine Learning Algorithm: A Forecasting Experiment for Scotland. International Journal of Environmental Research and Public Health. 2024; 21(7):841. https://doi.org/10.3390/ijerph21070841
Chicago/Turabian StyleTzitiridou-Chatzopoulou, Maria, Georgia Zournatzidou, and Michael Kourakos. 2024. "Predicting Future Birth Rates with the Use of an Adaptive Machine Learning Algorithm: A Forecasting Experiment for Scotland" International Journal of Environmental Research and Public Health 21, no. 7: 841. https://doi.org/10.3390/ijerph21070841