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Multi-population mortality forecasting using tensor decomposition

Dong, Y., Huang, F., Yu, H. & Haberman, S. ORCID: 0000-0003-2269-9759 (2020). Multi-population mortality forecasting using tensor decomposition. Scandinavian Actuarial Journal, 2020(8), pp. 754-775. doi: 10.1080/03461238.2020.1740314


In this paper, we formulate the multi-population mortality forecasting problem based on 3-way (age, year, and country/gender) decompositions. By applying the canonical polyadic decomposition (CPD) and the different forms of the Tucker decomposition to multi-population mortality data (10 European countries and 2 genders), we find that the out-of-sample forecasting performance is significantly improved both for individual populations and the aggregate population compared with using the single-population mortality model based on rank-1 singular value decomposition (SVD), or the Lee–Carter model. The results also shed lights on the similarity and difference of mortality among different countries. Additionally, we compare the variance-explained method and the out-of-sample validation method for rank (hyper-parameter) selection. Results show that the out-of-sample validation method is preferred for forecasting purposes.

Publication Type: Article
Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis in Scandinavian Actuarial Journal on 14 Mar 2020, available online:
Publisher Keywords: Multi-population mortality forecasting, tensor decomposition, CPD, Tucker, SVD
Subjects: H Social Sciences > HF Commerce > HF5601 Accounting
Departments: Bayes Business School > Actuarial Science & Insurance
SWORD Depositor:
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