In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

https://northumbria-test.eprints-hosting.org/id/document/266028

Future life expectancy in 35 industrialised countries: projections with a Bayesian model ensemble

Continuing increases in life expectancy beyond previously-held limits have brought to the fore the critical importance of mortality forecasting. Significant developments in mortality forecasting since 1980 are reviewed under three broad approaches: expectation, extrapolation and explanation. Expectation is not generally a good basis for mortality forecasting, as it is subjective; expert expectations are invariably conservative. Explanation is restricted to certain causes of death with known determinants. Decomposition by cause of death poses problems associated with the lack of independence among causes and data difficulties. Most developments have been in extrapolative forecasting, and make use of statistical methods rather than models developed primarily for age-specific graduation. Methods using two-factor models (age-period or age-cohort) have been most successful. The two-factor Lee–Carter method, and, in particular, its variants, have been successful in terms of accuracy, while recent advances have improved the estimation of forecast uncertainty. Regression-based (GLM) methods have been less successful, due to nonlinearities in time. Three-factor methods are more recent; the Lee–Carter age-period-cohort model appears promising. Specialised software has been developed and made available. Research needs include further comparative evaluations of methods in terms of the accuracy of the point forecast and its uncertainty, encompassing a wide range of mortality situations.

/pdf/mortality-modelling-and-forecasting-a-review-of-methods-57ywz2mf1x.pdf

Mortality modelling and forecasting: a review of methods

Demographic forecasting: 1980 to 2005 in review

Continuing increases in life expectancy beyond previously-held limits have brought to the fore the critical importance of mortality forecasting. Significant developments in mortality forecasting since 1980 are reviewed under three broad approaches: expectation, extrapolation and explanation. Expectation is not generally a good basis for mortality forecasting, as it is subjective; expert expectations are invariably conservative. Explanation is restricted to certain causes of death with known determinants. Decomposition by cause of death poses problems associated with the lack of independence among causes and data difficulties. Most developments have been in extrapolative forecasting, and make use of statistical methods rather than models developed primarily for age-specific graduation. Methods using two-factor models (age-period or age-cohort) have been most successful. The two-factor Lee^Carter method, and, in particular, its variants, have been successful in terms of accuracy, while recent advances have improved the estimation of forecast uncertainty. Regression-based (GLM) methods have been less successful, due to nonlinearities in time. Three-factor methods are more recent; the Lee^Carter age-periodcohort model appears promising. Specialised software has been developed and made available. Research needs include further comparative evaluations of methods in terms of the accuracy of the point forecast and its uncertainty, encompassing a wide range of mortality situations.

/pdf/mortality-modelling-and-forecasting-a-review-of-methods-by-h-59z7kwtdti.pdf

MORTALITY MODELLING AND FORECASTING: A REVIEW OF METHODS By H. Booth and L. Tickle

https://robjhyndman.com/papers/isi2005.pdf

Robust forecasting of mortality and fertility rates: A functional data approach

We propose a new method for forecasting age-specific mortality and fertility rates observed over time. Our approach allows for smooth functions of age, is robust for outlying years due to wars and epidemics, and provides a modelling framework that is easily adapted to allow for constraints and other information. We combine ideas from functional data analysis, nonparametric smoothing and robust statistics to form a methodology that is widely applicable to any functional time series data, and age-specific mortality and fertility in particular. We show that our model is a generalization of the Lee-Carter model commonly used in mortality and fertility forecasting. The methodology is applied to French mortality data and Australian fertility data, and we show that the forecasts obtained are superior to those from the Lee-Carter method and several of its variants.

Robust forecasting of mortality and fertility rates: a functional data approach

Currently, fuel stations are operated manually, with a controlling unit that performs a variety of tasks. Manual fuel stations take longer to set up and require a lot of manpower. Furthermore, it is prone to errors and has a higher probability of human-caused errors. Fuel stations cannot be built in remote areas because of these restrictions. The primary purpose of this study is to build an automated fuel dispensing system that integrates RFID technology and a GSM module to address all of the problems highlighted. A customer can use an RFID-based prepaid card system to access fuel at fuel stations with this system. When the consumers need to fill their tank from a fuel dispenser, they must first place their RFID tag in close proximity to the RFID reader before entering the amount. The Arduino reads the information from the RFID system and acts accordingly depending on the requirement of the customer, as well as deducting the appropriate expanses from the user's card. Furthermore, if a consumer attempts to scan an unregistered card, the RFID system will refuse the transaction. The system can calculate the amount of fuel dispensed from the user's RFID card automatically, and send that information, as well as the card's remaining balance, to the customer's phone via GSM technology. In the event that the tag's balance falls below a certain threshold, the customer is given the option to recharge it. To store all types of data related to the fuel dispensing system, the system employs an offline database system. The RFID and Arduino Mega are used to improve the current fuel dispensing system by decreasing human labor and providing an auto-guided mechanism to complete tasks in a systematic manner. As a result, the system is extremely reliable and time saving.

Design and Implementation of a Data-Driven Fuel Management System

Forward and reverse recovery are necessary factors of diode performance. This analysis demonstrates how the forward recovery properties of a PiN diode changed after the semiconductor materials were altered by Silicon (Si), Germanium (Ge), as well as Silicon Carbide (SiC) material. The diode industry, like that of other semiconductor devices, is dominated by Si,; nevertheless, SiC has recently gained popularity due to its several advantages, and Ge is also deployed in some cases. However, the results of the complete analyses in this work illustrate which of the three PiN diodes of the material type has the best forward recovery characteristics under particular parameters and setups by the COMSOL Multiphysics 5.5 software. The PiN diode, which is Ge based shows a faster forward recovery time, tfr, about 50ns, comparable to 110ns for the Si based PiN diode and 200ns for the SiC based PiN diode for a given current density rate, these are roughly twice and four times the Ge based PiN diode. Apart from that, the maximum forward recovery voltage drop of the Ge PiN diode is lower than the other two, thus it would have lower power loss than other two.

Md. Shahid Ullah

Papers

Robust forecasting of mortality and fertility rates: A functional data approach

Robust forecasting of mortality and fertility rates: a functional data approach

Design and Implementation of a Data-Driven Fuel Management System

Changes on Forward Recovery among Si, Ge and SiC-Based PiN Diode