The benefits of social insurance system prediction using a hybrid fuzzy time series method.

IF 3.5 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
PeerJ Computer Science Pub Date : 2024-11-26 eCollection Date: 2024-01-01 DOI:10.7717/peerj-cs.2500
Ahmed Abdelreheem Khalil, Mohamed Abdelaziz Mandour, Ahmed Ali
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引用次数: 0

Abstract

Decision-making in many industries relies heavily on accurate forecasts, including the insurance sector. The Social Insurance System (SIS) in Egypt, operating under a fully funded paradigm, depends on reliable predictions to ensure effective financial planning. This research introduces a hybrid predictive model that combines fuzzy time series (FTS) Markov chains with the tree partition method (TPM) and difference transformation to forecast total pension benefits within Egypt's SIS. A key feature of the proposed model is its ability to optimize the partitioning process, resulting in the creation of nine intervals that reduce computational complexity while maintaining forecasting accuracy. These intervals were consistently applied across all fuzzy time series models for comparison. The model's performance is evaluated using established metrics such as MAPE, Thiels' U statistic, and RMSE. Additionally, prediction interval coverage probability (PICP) and mean prediction interval length (MPIL) are used to assess the quality of prediction intervals, with a 95% prediction interval serving as the baseline. The proposed model achieved a PICP of approximately 95%, indicating well-calibrated prediction intervals, although the MPIL of 424.5 reflects a wider uncertainty range. Despite this, the model balances coverage accuracy and interval precision effectively. The results demonstrate that the proposed model significantly outperforms traditional models like linear regression, ARIMA, and exponential smoothing and conventional FTS models like Song, Chen, Yu, and Cheng by achieving the lowest MAPE with the value of 11.8% for training and 10.65% for testing. This superior performance highlights the model's reliability and potential applicability to further forecasting tasks in the field of insurance and beyond.

使用混合模糊时间序列法预测社会保险系统的益处。
许多行业的决策在很大程度上依赖于准确的预测,包括保险业。埃及的社会保险制度(SIS)在资金充足的模式下运作,依靠可靠的预测来确保有效的财务规划。本研究引入模糊时间序列(FTS)马尔可夫链与树分割法(TPM)和差异变换相结合的混合预测模型,对埃及SIS的养老金总收益进行预测。所提出的模型的一个关键特征是它能够优化划分过程,从而创建9个区间,在保持预测准确性的同时降低计算复杂性。这些区间一致地应用于所有模糊时间序列模型进行比较。模型的性能使用既定的指标进行评估,如MAPE、thiel’U统计量和RMSE。以预测区间覆盖概率(PICP)和平均预测区间长度(MPIL)作为预测区间质量的评价指标,以95%的预测区间为基准。该模型的PICP约为95%,表明了校准良好的预测区间,尽管MPIL为424.5反映了更大的不确定性范围。尽管如此,该模型有效地平衡了覆盖精度和区间精度。结果表明,该模型显著优于传统的线性回归、ARIMA、指数平滑等模型和传统的FTS模型(Song、Chen、Yu、Cheng),实现了最低的MAPE,训练的MAPE为11.8%,测试的MAPE为10.65%。这种优越的性能突出了模型的可靠性和潜在的适用性,进一步预测任务在保险领域和超越。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
PeerJ Computer Science
PeerJ Computer Science Computer Science-General Computer Science
CiteScore
6.10
自引率
5.30%
发文量
332
审稿时长
10 weeks
期刊介绍: PeerJ Computer Science is the new open access journal covering all subject areas in computer science, with the backing of a prestigious advisory board and more than 300 academic editors.
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