A State Space Framework for Automatic Forecasting Using Exponential Smoothing Methods.

We provide a new approach to automatic business forecasting based on an extended range of exponential smoothing methods. Each method in our taxonomy of exponential smoothing methods can be shown to be equivalent to the forecasts obtained from a state space model. This allows (1) the easy calculation of the likelihood, the AIC and other model selection criteria; (2) the computation of prediction intervals for each method; and (3) random simulation from the underlying state space model. We demonstrate the methods by applying them to the data from the M-competition on the M3-competition.Automatic forecasting, exponential smoothing, prediction intervals, state space models.

The Devon Island Expedition 1960-64

The establishment of the Arctic Institute's Devon Island Base Station and the progress of the research program in 1960 and 1961 were reported in brief summaries and preliminary field reports in Arctic 13:270-71 and 14:252-65, and a review of the research from September 1961 to September 1962 appeared in Arctic 15:317-320. Preliminary field reports for that period are presented here. ..

Model confidence sets and forecast combination: an application to age-specific mortality

Background: Model averaging combines forecasts obtained from a range of models, and it often produces more accurate forecasts than a forecast from a single model. Objective: The crucial part of forecast accuracy improvement in using the model averaging lies in the determination of optimal weights from a finite sample. If the weights are selected sub-optimally, this can affect the accuracy of the model-averaged forecasts. Instead of choosing the optimal weights, we consider trimming a set of models before equally averaging forecasts from the selected superior models. Motivated by Hansen et al. (2011), we apply and evaluate the model confidence set procedure when combining mortality forecasts. Data & Methods: The proposed model averaging procedure is motivated by Samuels and Sekkel (2017) based on the concept of model confidence sets as proposed by Hansen et al. (2011) that incorporates the statistical significance of the forecasting performance. As the model confidence level increases, the set of superior models generally decreases. The proposed model averaging procedure is demonstrated via national and sub-national Japanese mortality for retirement ages between 60 and 100+. Results: Illustrated by national and sub-national Japanese mortality for ages between 60 and 100+, the proposed model-average procedure gives the smallest interval forecast errors, especially for males. Conclusion: We find that robust out-of-sample point and interval forecasts may be obtained from the trimming method. By robust, we mean robustness against model misspecification

Simulation framework in fertility projections

By 1951, average fertility had fallen to just over two children per woman, and only five percent of children would die in their first ten years of life. A similar pattern of declining fertility and mortality rates, collectively known as the demographic transition, has been observed in every industrializing country. Financial projections for Social Security systems depend on many demographic, economic and social factors as well as the reduction of fertility rates and the ageing of a population. In order to address the need to develop reliable projections, it is unavoidable to detect appropriate measures to represent the future trends of the quantities of interest. The aim of the paper is apply to Italian data a mathematical scheme suitable for projecting the fertility rates and for measuring the uncertainty around these estimates. Finally a numerical application is provided