Grouped Functional Time Series Forecasting: An Application to Age-Specific Mortality Rates

AbstractWhen modelling subnational mortality rates, we should consider three features: (1) how to incorporate any possible correlation among subpopulations to potentially improve forecast accuracy through multi-population joint modelling; (2) how to reconcile subnational mortality forecasts so that they aggregate adequately across various levels of a group structure; (3) among the forecast reconciliation methods, how to combine their forecasts to achieve improved forecast accuracy. To address these issues, we introduce an extension of grouped univariate functional time-series method. We first consider a multivariate functional time-series method to jointly forecast multiple related series. We then evaluate the impact and benefit of using forecast combinations among the forecast reconciliation methods. Using the Japanese regional age-specific mortality rates, we investigate 1–15-step-ahead point and interval forecast accuracies of our proposed extension and make recommendations.

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Nonparametric trend estimation in functional time series with application to annual mortality rates

Biometrics ◽

10.1111/biom.13353 ◽

2020 ◽

Author(s):

Israel Martínez‐Hernández ◽

Marc G. Genton

Keyword(s):

Time Series ◽

Mortality Rates ◽

Trend Estimation ◽

Functional Time Series ◽

Annual Mortality

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Forecasting Age-Specific Brain Cancer Mortality Rates Using Functional Data Analysis Models

Advances in Epidemiology ◽

10.1155/2015/721592 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Keshav P. Pokhrel ◽

Chris P. Tsokos

Keyword(s):

Time Series ◽

Data Analysis ◽

State Space ◽

Functional Data Analysis ◽

Brain Cancer ◽

Functional Data ◽

Cancer Mortality ◽

Mortality Rates ◽

Exponential Smoothing ◽

Functional Time Series

Incidence and mortality rates are considered as a guideline for planning public health strategies and allocating resources. We apply functional data analysis techniques to model age-specific brain cancer mortality trend and forecast entire age-specific functions using exponential smoothing state-space models. The age-specific mortality curves are decomposed using principal component analysis and fit functional time series model with basis functions. Nonparametric smoothing methods are used to mitigate the existing randomness in the observed data. We use functional time series model on age-specific brain cancer mortality rates and forecast mortality curves with prediction intervals using exponential smoothing state-space model. We also present a disparity of brain cancer mortality rates among the age groups together with the rate of change of mortality rates. The data were obtained from the Surveillance, Epidemiology and End Results (SEER) program of the United States. The brain cancer mortality rates, classified under International Classification Disease code ICD-O-3, were extracted from SEER*Stat software.

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