Forecasting Mortality: A Parameterized Time Series Approach

Forecasting mortality: a parameterized time series approach.

This article links parameterized model mortality schedules with time series methods to develop forecasts of U.S. mortality to the year 2000. The use of model mortality schedules permits a relatively concise representation of the history of mortality by age and sex from 1900 to 1985, and the use of modern time series methods to extend this history forward to the end of this century allows for a ...

Time Series Forecasting: a Nonlinear Dynamics Approach

The problem of prediction of a given time series is examined on the basis of recent nonlinear dynamics theories. Particular attention is devoted to forecast the amplitude and phase of one of the most common solar indicator activity, the international monthly smoothed sunspot number. It is well known that the solar cycle is very difficult to predict due to the intrinsic complexity of the related...

Time Variant Fuzzy Time Series Approach for Forecasting Using Particle Swarm Optimization

  Fuzzy time series have been developed during the last decade to improve the forecast accuracy. Many algorithms have been applied in this approach of forecasting such as high order time invariant fuzzy time series. In this paper, we present a hybrid algorithm to deal with the forecasting problem based on time variant fuzzy time series and particle swarm optimization algorithm, as a highly effi...

a time series model for assessing the trend and forecasting the road traffic accident mortality

conclusions there was a decreasing trend in the study and the future years. it seems that implementation of some interventions in the recent decade has had a positive effect on the decline of rta fatalities. nevertheless, there is still a need to pay more attention in order to prevent the occurrence and the mortalities related to traffic accidents. results the mean age of the victims was 37.22 ...

A Fuzzy-LP Approach in Time Series Forecasting