multipopulation_cv: Function to apply cross-validation techniques for testing the...

In CvmortalityMult: Cross-Validation for Multi-Population Mortality Models

Function to apply cross-validation techniques for testing the forecasting accuracy of multi-population mortality models

Description

R function for testing the accuracy out-of-sample using different cross-validation techniques. The multi-population mortality models used by the package are: additive (Debon et al., 2011), multiplicative (Russolillo et al., 2011), common-factor (CFM) (Carter and Lee, 1992), joint-k (Carter and Lee, 2011), and augmented-common-factor (ACFM) (Li and Lee, 2005). We provide a R function that employ the cross-validation techniques for three-way-array, following the preliminary idea for panel-time series, specifically for testing the forecasting ability of single mortality models (Atance et al. 2020). These techniques consist on split the database in two parts: training set (to run the model) and test set (to check the forecasting accuracy of the model). This procedure is repeated several times trying to check the forecasting accuracy in different ways. With this function, the user can provide its own mortality rates for different populations and apply different cross-validation techniques. The user must specify three main inputs in the function (nahead, trainset1, and fixed_train_origin) to apply a specific cross-validation technique between the different options. Indeed, you can apply the next time-series cross-validation techniques, following the terminology employed by Bergmeir et al. (2012):

1. Fixed-Origin. The technique chronologically splits the data set into two parts, first for training the model, and second for testing the forecasting accuracy. This process predicts only once for different forecast horizons which are evaluated to assess the accuracy of the multi-population model, as can be seen in the next Figure.

The function "multipopulation_cv()" understands FIXED-ORIGIN when trainset1 + nahead = number of provided periods and fixed_train_origin = TRUE (default value). As an example, data set with periods from 1991 to 2020, trainset1 = 25 and nahead= 5, with a total of 30, equals to length of the periods 1991:2020.

1. Rolling-Origin recalibration (RO-recalibration) evaluation. In this technique, the data set is spitted into 'k' sub-sets of data, keeping chronologically order. The first set of data corresponds to the training set where the model is fitted and the forecast are evaluated with a fixed horizon. In every iteration, the model is enlarged and recalibrated adding the test-set periods (nahead in the function) to the training set and forecasting the next fixed horizon. The idea is to keep the origin fixed and move the forecast origin in every iteration, as can be seen in the next Figure

In the package, to apply this technique the users must provided a value of trainset1 higher than two (to meet with the minimum time-series size), and fixed_train_origin = TRUE (default value), independently of the assigned value of nahead. There are different resampling techniques that can be applied based on the values of trainset1 and nahead. Indeed, when nahead = 1 — Leave-One-Out-Cross-Validation (LOOCV) with RO-recalibration will be applied. Independently, of the number of periods in the first train set (trainset1). When, nahead and trainset1 are equal — K-Fold-Cross-Validation (LOOCV) with RO-recalibration will be applied. For the rest values of nahead and trainset1 a standard time-series CV technique will be implemented.

1. Rolling-Window (RW) evaluation The approach is very similar to the RO-recalibration, but maintaining the training set size constant at each forecast/iteration. Maintaining the chronological order in each forecast, the training set adds the previous. projected periods of the test set and discards the earliest observations, as can be seen in the next Figure.

To apply this technique, the multipopulation_cv() function requires that fixed_train_origin = c("FALSE", "1"), regardless of the values of nahead and trainset1. Equally as in RO-recalibration, LOOCV, and k-fold can be applied with nahead = 1, or nahead equals to trainset1, respectively, but keeping the training set constant through the iterations. Additionally, the common time-series CV approach can be applied for different values of nahead and trainset1. When fixed_train_origin = "FALSE", at each iteration the training set adds the next nahead periods and discards the oldest keeping the training set size constant. While fixed_train_origin = "1", at every iteration the training set only incorporates the next period ahead and discards only the latest period; maintaining the length of the training set constant and allowing to assess the forecasting accuracy of the mortality models in the long and medium term with different periods.

It should be mentioned that this function is developed for cross-validation the forecasting accuracy of several populations. However, in case you only consider one population, the function will forecast the Lee-Carter model for one population. To test the forecasting accuracy of the selected model, the function provides five different measures: SSE, MSE, MAE, MAPE or All. This measure of accuracy will be provided in different ways: a total measure, among ages considered, among populations and among projected blocked (periods). Depending on how you want to check the forecasting accuracy of the model you could select one or other. In this case, the measures will be obtained using the mortality rates in the normal scale as recommended by Santolino (2023) against the log scale.

Usage

multipopulation_cv(
  qxt,
  model = c("additive", "multiplicative", "CFM", "joint-K", "ACFM"),
  periods,
  ages,
  nPop,
  lxt = NULL,
  ktmethod = c("Arimapdq", "arima010"),
  nahead,
  trainset1,
  fixed_train_origin = TRUE,
  measures = c("SSE", "MSE", "MAE", "MAPE", "All"),
  ...
)

Arguments

qxt	mortality rates used to fit the multi-population mortality models. This rates can be provided in matrix or in data.frame.
model	multi-population mortality model chosen to fit the mortality rates c("additive", "multiplicative", "CFM", "joint-K", "ACFM"). In case you do not provide any value, the function will apply the "additive" option.
periods	number of years considered in the fitting in a vector way c(minyear:maxyear).
ages	vector with the ages considered in the fitting. If the mortality rates provide from an abridged life tables, it is necessary to provide a vector with the ages, see the example.
nPop	number of population considered for fitting.
lxt	survivor function considered for every population, not necessary to provide.
ktmethod	method used to forecast the value of kt Arima(p,d,q) or ARIMA(0,1,0); c("Arimapdq", "arima010").
nahead	is a vector specifying the number of periods to forecast nahead periods ahead. It should be noted that when nahead is equal to trainset1 a k-fold CV will be applied. Whereas when nahead is equal to 1, the CV process will be a Leave-One-Out CV.
trainset1	is a vector with the periods for the first training set. This value must be greater than 2 to meet the minimum time series size (Hyndman and Khandakar, 2008).
fixed_train_origin	option to select whether the origin in the first train set is fixed or not. The default value is TRUE where the origin of the first training sets is fixed. The alternatives are: FALSE when the first train set is moved in every iteration according to the provided nahead value, and 2. 1 when the train set is moved one period ahead in every repetition keeping constant the amount of data, and incorporating the next period observation, and discarding the last available period.
measures	choose the non-penalized measure of forecasting accuracy that you want to use; c("SSE", "MSE", "MAE", "MAPE", "All"). Check the function. In case you do not provide any value, the function will apply the "SSE" as measure of forecasting accuracy.
...	other arguments for iarima.

Value

An object of the class "MultiCv" including a list() with different components of the cross-validation process:

• ax parameter that captures the average shape of the mortality curve in all considered populations.

• bx parameter that explains the age effect x with respect to the general trend kt in the mortality rates of all considered populations.

• kt.fitted obtained values for the tendency behavior captured by kt .

• kt.future future values of kt for every iteration in the cross-validation.

• kt.arima the arima selected for each kt time series.

• Ii parameter that captures the differences in the pattern of mortality in any region i with respect to Region 1.

• formula multi-population mortality formula used to fit the mortality rates.

• model provided the model selected in every case.

• nPop provided number of populations to fit the periods.

• qxt.crude corresponds to the crude mortality rates. These crude rate are directly obtained by dividing the number of registered deaths by the number of those initially exposed to the risk for age x, period t and in each region i.

• qxt.future future mortality rates estimated with the multi-population mortality model.

• logit.qxt.future future mortality rates in logit way estimated with the multi-population mortality model.

• meas_ages measure of forecasting accuracy through the ages of the study.

• meas_periodsfut measure of forecasting accuracy in every forecasting period(s) of the study.

• meas_pop measure of forecasting accuracy through the populations considered in the study.

• meas_total a global measure of forecasting accuracy through the ages, periods and populations of the study.

• warn_msgs vector with the populations where the model has not converged.

References

Atance, D., Debon, A., and Navarro, E. (2020). A comparison of forecasting mortality models using resampling methods. Mathematics 8(9): 1550.

Bergmeir, C. & Benitez, J.M. (2012) On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192–

Carter, L.R. and Lee, R.D. (1992). Modeling and forecasting US sex differentials in mortality. International Journal of Forecasting, 8(3), 393–411.

Debon, A., & Atance, D. (2022). Two multi-population mortality models: A comparison of the forecasting accuracy with resampling methods. in Contributions to Risk Analysis: Risk 2022. Fundacion Mapfre

Debon, A., Montes, F., & Martinez-Ruiz, F. (2011). Statistical methods to compare mortality for a group with non-divergent populations: an application to Spanish regions. European Actuarial Journal, 1, 291-308.

Lee, R.D. & Carter, L.R. (1992). Modeling and forecasting US mortality. Journal of the American Statistical Association, 87(419), 659–671.

Li, N. and Lee, R.D. (2005). Coherent mortality forecasts for a group of populations: An extension of the Lee-Carter method. Demography, 42(3), 575–594.

Russolillo, M., Giordano, G., & Haberman, S. (2011). Extending the Lee–Carter model: a three-way decomposition. Scandinavian Actuarial Journal, 96-117.

Santolino, M. (2023). Should Selection of the Optimum Stochastic Mortality Model Be Based on the Original or the Logarithmic Scale of the Mortality Rate?. Risks, 11(10), 170.

See Also

fitLCmulti, forecast.fitLCmulti, plot.fitLCmulti, plot.forLCmulti, MeasureAccuracy.

Examples

#The example takes more than 5 seconds because they include
#several cross-validation methods and hence all the processes are included in "donttest".

#We present a cross-validation method for spanish male regions using:

ages <- c(0, 1, 5, 10, 15, 20, 25, 30, 35, 40,
         45, 50, 55, 60, 65, 70, 75, 80, 85, 90)
library(gnm)
library(forecast)
library(StMoMo)

#1. FIXED-ORIGIN -- using the ACFM nahead + trainset1 = periods;
#fixed_train_origin = TRUE (defualt value)
ho_Spainmales_addit <- multipopulation_cv(qxt = SpainRegions$qx_male,
                                         model = c("ACFM"),
                                         periods =  c(1991:2020), ages = c(ages),
                                         nPop = 18, lxt = SpainRegions$lx_male,
                                         nahead = 5,
                                         trainset1 = 25,
                                         ktmethod = c("Arimapdq"),
                                         measures = c("SSE"))
ho_Spainmales_addit

#Once, we have run the function we can check the result in different ways:
ho_Spainmales_addit$meas_ages
ho_Spainmales_addit$meas_periodsfut
ho_Spainmales_addit$meas_pop
ho_Spainmales_addit$meas_total

#2. Let's continue with a RO-recalibration,
#(fixed_train_origin = TRUE (defualt value))
#where we have implemented three main CV techniques:
#2.1. Leave-One-Out-Cross-Validation (LOOCV) RO-recalibration when nahead = 1;
#(independently the number of periods blocked for the first train set; trainset1"
loocv_Spainmales_addit <- multipopulation_cv(qxt = SpainRegions$qx_male,
                                         model = c("additive"),
                                         periods =  c(1991:2020), ages = c(ages),
                                         nPop = 18, lxt = SpainRegions$lx_male,
                                         nahead = 1, trainset1 = 10,
                                         ktmethod = c("Arimapdq"),
                                         measures = c("SSE"))
loocv_Spainmales_addit

#Once, we have run the function we can check the result in different ways:
loocv_Spainmales_addit$meas_ages
loocv_Spainmales_addit$meas_periodsfut
loocv_Spainmales_addit$meas_pop
loocv_Spainmales_addit$meas_total

#2.2. K-Fold-CV RO-recalibration when nahead = trainset1
kfoldcv_Spainmales_addit <- multipopulation_cv(qxt = SpainRegions$qx_male,
                                         model = c("additive"),
                                         periods =  c(1991:2020), ages = c(ages),
                                         nPop = 18, lxt = SpainRegions$lx_male,
                                         nahead = 5, trainset1 = 5,
                                         ktmethod = c("Arimapdq"),
                                         measures = c("SSE"))
kfoldcv_Spainmales_addit

#Once, we have run the function we can check the result in different ways:
kfoldcv_Spainmales_addit$meas_ages
kfoldcv_Spainmales_addit$meas_periodsfut
kfoldcv_Spainmales_addit$meas_pop
kfoldcv_Spainmales_addit$meas_total

#2.3. standard time-series CV
cv_Spainmales_addit <- multipopulation_cv(qxt = SpainRegions$qx_male,
                                         model = c("additive"),
                                         periods =  c(1991:2020), ages = c(ages),
                                         nPop = 18, lxt = SpainRegions$lx_male,
                                         nahead = 5, trainset1 = 10,
                                         fixed_train_origin = "TRUE",
                                         ktmethod = c("Arimapdq"),
                                         measures = c("SSE"))

cv_Spainmales_addit
#Once, we have run the function we can check the result in different ways:
cv_Spainmales_addit$meas_ages
cv_Spainmales_addit$meas_periodsfut
cv_Spainmales_addit$meas_pop
cv_Spainmales_addit$meas_total

#3. RW-evaluation (fixed_train_origin = c("FALSE", "1"))
#3.1. fixed_train_origin == "TRUE" (The default value)
#In this case, the previous processes (Fixed-Origin or RO-recalibration)
#3.2. fixed_train_origin == "FALSE"
#where the origin in the training set is moved "nahead" period ahead in every iteration.
#This process allows to test the forecasting accuracy of "nahead" periods ahead
#keeping constant the size of the training and test set. As an example, we present
#three methods
#3.2.1. LOOCV
loocv_Spainmales_addit_rw <- multipopulation_cv(qxt = SpainRegions$qx_male,
                                         model = c("additive"),
                                         periods =  c(1991:2020), ages = c(ages),
                                         nPop = 18, lxt = SpainRegions$lx_male,
                                         nahead = 1, trainset1 = 10,
                                         fixed_train_origin = "FALSE",
                                         ktmethod = c("Arimapdq"),
                                         measures = c("SSE"))

loocv_Spainmales_addit_rw

#Once, we have run the function we can check the result in different ways:
loocv_Spainmales_addit_rw$meas_ages
loocv_Spainmales_addit_rw$meas_periodsfut
loocv_Spainmales_addit_rw$meas_pop
loocv_Spainmales_addit_rw$meas_total

#3.2.2. K-Fold-CV
kfoldcv_Spainmales_addit_rw <- multipopulation_cv(qxt = SpainRegions$qx_male,
                                         model = c("additive"),
                                         periods =  c(1991:2020), ages = c(ages),
                                         nPop = 18, lxt = SpainRegions$lx_male,
                                         nahead = 5, trainset1 = 5,
                                         fixed_train_origin = "FALSE",
                                         ktmethod = c("Arimapdq"),
                                         measures = c("SSE"))

kfoldcv_Spainmales_addit_rw

#Once, we have run the function we can check the result in different ways:
kfoldcv_Spainmales_addit$meas_ages
kfoldcv_Spainmales_addit$meas_periodsfut
kfoldcv_Spainmales_addit$meas_pop
kfoldcv_Spainmales_addit$meas_total

#3.2.3. standard time-series CV
cv_Spainmales_addit_rw <- multipopulation_cv(qxt = SpainRegions$qx_male,
                                         model = c("additive"),
                                         periods =  c(1991:2020), ages = c(ages),
                                         nPop = 18, lxt = SpainRegions$lx_male,
                                         nahead = 5, trainset1 = 10,
                                         fixed_train_origin = "FALSE",
                                         ktmethod = c("Arimapdq"),
                                         measures = c("SSE"))

cv_Spainmales_addit_rw

#Once, we have run the function we can check the result in different ways:
cv_Spainmales_addit_rw$meas_ages
cv_Spainmales_addit_rw$meas_periodsfut
cv_Spainmales_addit_rw$meas_pop
cv_Spainmales_addit_rw$meas_total

#3.3  RW-evaluation (fixed_train_origin = c("1"))
#where the origin in the training set is moved 1 period ahead in every iteration.
#This process allows to test the forecasting accuracy of "nahead" periods ahead
#modifying the origin in the training set by 1.
#When "nahead" = 1 --- we will have a loocv equally as in the previous process,
#while using a different value of 1 for "nahead" we will test the forecasting
#accuracy of the model in "nahead" periods:
cv_Spainmales_addit_rw1 <- multipopulation_cv(qxt = SpainRegions$qx_male,
                                         model = c("additive"),
                                         periods =  c(1991:2020), ages = c(ages),
                                         nPop = 18, lxt = SpainRegions$lx_male,
                                         nahead = 5, trainset1 = 15,
                                         fixed_train_origin = "1",
                                         ktmethod = c("Arimapdq"),
                                         measures = c("SSE"))
cv_Spainmales_addit_rw1

#Once, we have run the function we can check the result in different ways:
cv_Spainmales_addit_rw1$meas_ages
cv_Spainmales_addit_rw1$meas_periodsfut
cv_Spainmales_addit_rw1$meas_pop
cv_Spainmales_addit_rw1$meas_total

CvmortalityMult documentation built on April 4, 2025, 5:20 a.m.