This article describes the use of Bayesian methods in the statistical analysis of time series. The use of Markov chain Monte Carlo methods has made even the more complex time series models amenable to Bayesian analysis. Models discussed in some detail are ARIMA models and their fractionally integrated counterparts, state-space models, Markov switching and mixture models, and models allowing for...

This article describes the use of Bayesian methods in the statistical analysis of time series. The use of Markov chain Monte Carlo methods has made even the more complex time series models amenable to Bayesian analysis. Models discussed in some detail are ARIMA models and their fractionally integrated counterparts, state-space models, Markov switching and mixture models, and models allowing for...

The analysis of economic time series is central to a wide range of applications, including business cycle measurement, financial risk management, policy analysis based on structural dynamic econometric models, and forecasting. This article provides an overview of the problems of specification, estimation and inference in linear stationary and ergodic time series models as well as non-stationary...

T000032 Any series of observations ordered along a single dimension, such as time, may be thought of as a time series. The emphasis in time series analysis is on studying the dependence among observations at different points in time. What distinguishes time series analysis from general multivariate analysis is precisely the temporal order imposed on the observations. Many economic variables, su...

Some facts about sines and cosines: • cos(A+B) = cos(A) cos(B)− sin(A) sin(B) • sin(A+B) = sin(A) cos(B) + cos(A) sin(B) • Cosine is an even function: cos(−θ) = cos(θ) • Sine is an odd function: sin(−θ) = − sin(θ) • cos(ωt− π/2) = sin(ωt) • R cos(ωt + φ) = R sin(φ) cos(ωt) + R cos(φ) sin(ωt), and any wave form can be written as a linear combination of sines and cosines. Similarly, A sin(ωt) +B ...

Intelligent data analysis often requires one to extract meaningful conclusions about a complicated system using data from a single sensor. If the system is linear, a wealth of well-established, powerful time-series analysis techniques is available to the analyst. If it is not, the problem is much harder and one must resort to nonlinear dynamics theory in order to infer useful information from t...

Preface The course Time series analysis is based on the book [7] and replaces our previous course Stationary stochastic processes which was based on [6]. The books, and by that the courses, differ in many respects, the most obvious is that [7] is more applied that [6]. The word " applied " is partly a fine word for " elementary ". A very important part of the course consists in looking at and t...

The world is witnessing a dramatic shift of settlement pattern from rural to urban population, particularly in developing countries. The rapid Addis Ababa urbanization reflects this global phenomenon and the subsequent socio-economic and environmental impacts, are causing massive public uproar and political instability. The objective of this study was to use remotely sensed Landsat data to iden...