scholarly journals FORECASTING MULTIPLE FUNCTIONAL TIME SERIES IN A GROUP STRUCTURE: AN APPLICATION TO MORTALITY

2020 ◽  
Vol 50 (2) ◽  
pp. 357-379 ◽  
Author(s):  
Han Lin Shang ◽  
Steven Haberman
Keyword(s):  
Time Series ◽  
Group Structure ◽  
Forecast Accuracy ◽  
Mortality Rates ◽  
Series Method ◽  
Functional Time Series ◽  
Forecast Combinations ◽  
Time Series Method ◽  
Mortality Forecasts ◽  
The Impact

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.

scholarly journals Is the Group Structure Important in Grouped Functional Time Series?

2021 ◽  
pp. 1-22
Author(s):  
Yang Yang ◽  
Han Lin Shang
Keyword(s):  
Time Series ◽  
Group Structure ◽  
Forecast Accuracy ◽  
Joint Modeling ◽  
Multiple Series ◽  
Functional Time Series ◽  
Modeling And Forecasting ◽  
Time Series Method ◽  
Group Structures ◽  
Practical Question

We study the importance of group structure in grouped functional time series. Due to the non-uniqueness of group structure, we investigate different disaggregation structures in grouped functional time series. We address a practical question on whether or not the group structure can affect forecast accuracy. Using a dynamic multivariate functional time series method, we consider joint modeling and forecasting multiple series. Illustrated by Japanese sub-national age-specific mortality rates from 1975 to 2016, we investigate one- to 15-step-ahead point and interval forecast accuracies for the two group structures.

scholarly journals Forecasting Seasonal Tourism Demand Using a Multiseries Structural Time Series Method

2017 ◽  
Vol 58 (1) ◽  
pp. 92-103 ◽  
Author(s):  
Jason Li Chen ◽  
Gang Li ◽  
Doris Chenguang Wu ◽  
Shujie Shen
Keyword(s):  
Time Series ◽  
Demand Forecasting ◽  
Forecast Accuracy ◽  
Series Method ◽  
Tourism Demand ◽  
Multivariate Method ◽  
Structural Time Series ◽  
Alternative Approach ◽  
Time Series Method ◽  
Structural Time

Multivariate forecasting methods are intuitively appealing since they are able to capture the interseries dependencies, and therefore may forecast more accurately. This study proposes a multiseries structural time series method based on a novel data restacking technique as an alternative approach to seasonal tourism demand forecasting. The proposed approach is analogous to the multivariate method but only requires one variable. In this study, a quarterly tourism demand series is split into four component series, each component representing the demand in a particular quarter of each year; the component series are then restacked to build a multiseries structural time series model. Empirical evidence from Hong Kong inbound tourism demand forecasting shows that the newly proposed approach improves the forecast accuracy, compared with traditional univariate models.

Waveform computations by the time-series method

1952 ◽  
Vol 1952 (10) ◽  
pp. 246-246 ◽  
Author(s):  
N.W. Lewis
Keyword(s):  
Time Series ◽  
Series Method ◽  
Time Series Method

scholarly journals Interrupted time series analyses to assess the impact of alcohol control policy on socioeconomic inequalities in mortality in Lithuania: a study protocol

2021 ◽  
Author(s):  
Jakob Manthey ◽  
Domantas Jasilionis ◽  
Huan Jiang ◽  
Olga Mesceriakova-Veliuliene ◽  
Janina Petkeviciene ◽  
...  
Keyword(s):  
Time Series ◽  
Research Ethics ◽  
Interrupted Time Series ◽  
Mortality Rates ◽  
Attributable Mortality ◽  
Population Census ◽  
Combining Data ◽  
Time Series Analyses ◽  
Inequalities In Mortality ◽  
The Impact

Introduction Alcohol use is a major risk factor for mortality. Previous studies suggest that the alcohol-attributable mortality burden is higher in lower socioeconomic strata. This project will test the hypothesis, that the 2017 increase of alcohol excise taxes for beer and wine, which was linked to lower all-cause mortality rates in previous analyses, will reduce socioeconomic mortality inequalities. Methods and analysis Data on all causes of deaths will be obtained from Statistics Lithuania. Record linkage will be implemented using personal identifiers combining data from 1) the 2011 whole-population census, 2) death records between March 1, 2011 (census date) and December 31, 2019, and 3) emigration records, for individuals aged 30 to 70 years. The analyses will be performed separately for all-cause and for alcohol-attributable deaths. Monthly age-standardized mortality rates will be calculated by sex, education, and three measures of socioeconomic status. Inequalities in mortality will be assessed using absolute and relative indicators between low and high SES groups. We will perform interrupted time series analyses, and test the impact of the 2017 rise in alcohol excise taxation using generalized additive mixed models. In these models, we will control for secular trends for economic development. Ethics and dissemination This work is part of project grant 1R01AA028224-01 by the National Institute on Alcohol Abuse and Alcoholism. It has been granted research ethics approval 050/2020 by CAMH Research Ethics Board on April 17, 2020, renewed on March 30, 2021.

scholarly journals IMPLEMENTASI METODE FUZZY TIME SERIES DENGAN MODEL ALGORITMA CHEN UNTUK MEMPREDIKSI HARGA EMAS

2020 ◽  
Vol 13 (1) ◽  
pp. 71-78
Author(s):  
Darsono Nababan ◽  
Eric Alexander
Keyword(s):  
Time Series ◽  
Interest Rates ◽  
Gold Price ◽  
Fuzzy Time Series ◽  
Actual Data ◽  
Series Method ◽  
Market Uncertainty ◽  
Low Interest Rates ◽  
Calculation Results ◽  
Time Series Method

Gold is one of the people's preferred forms of investment and is considered the safest (save -heaven). Gold risk which is considered small is the main attraction because in general Indonesian people are not yet familiar with capital market investments such as stocks and mutual funds. But the price of gold is very volatile as for the factors that affect the fluctuations of gold are consumption demand, volatility and market uncertainty, protection of low-interest rates, and the US dollar. Predicting the movement of the gold price and knowing where the direction of the exchange rate moves and determining the price of gold up or down cannot be done accurately and consistently. For this reason, in reducing the risk of loss, an application is needed to predict gold prices using the Fuzzy Time Series Chen algorithm using MATLAB software. In this study to obtain prediction results and comparison charts using actual data and prediction data for the 2015-2017 gold price. From the calculation results obtained by the prediction results with the Fuzzy Time Series method with the Chen algorithm where the average difference between the actual data and prediction data is not more than Rp. 2,850, - where predictions using the Fuzzy Time Series method Chen's algorithm is sufficient to use 1 data to predict the second data which makes this method accurate in predicting the price of gold.

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