Abstract
Recent mortality forecasting studies using artificial neural networks (ANNs) have shown improved forecasting performance compared with previous studies, where annual mortality rates were used. The use of annual mortality rates data leads to a problem in that the data are insufficient. Therefore, in this study, mortality rates were forecast by applying the related time-series data augmentation methods with an ANN, unlike in existing related studies. The experimental results showed that ANNs with augmented data have improved mortality forecasting performance compared to the case without data augmentation.
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Park, K., Park, J.S., Maeng, J., Shon, J.G. (2023). Mortality Forecasting Using Data Augmentation. In: Park, J.S., Yang, L.T., Pan, Y., Park, J.H. (eds) Advances in Computer Science and Ubiquitous Computing. CUTECSA 2022. Lecture Notes in Electrical Engineering, vol 1028. Springer, Singapore. https://doi.org/10.1007/978-981-99-1252-0_12
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DOI: https://doi.org/10.1007/978-981-99-1252-0_12
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