![]() If you are a member of an institution with an active account, you may be able to access content in one of the following ways: Get help with access Institutional accessĪccess to content on Oxford Academic is often provided through institutional subscriptions and purchases. Bayesian treatments based on simulation methods are also explored. The book shows that exact treatments become feasible when simulation-based methods such as importance sampling and particle filtering are adopted. Approximate methods include the extended Kalman filter and the more recently developed unscented Kalman filter. Part II discusses approximate and exact approaches for handling broad classes of non-Gaussian and nonlinear state space models. Part I then presents illustrations to real series and exercises are provided for a selection of chapters. The analysis can be carried out from both classical and Bayesian perspectives. The methods are based on the Kalman filter and are appropriate for a wide range of problems in practical time series analysis. Part I presents a full treatment of the construction and analysis of linear Gaussian state space models. The techniques that emerge from this approach are very flexible. The distinguishing feature of state space time series models is that observations are regarded as being made up of distinct components such as trend, seasonal, regression elements and disturbance elements, each of which is modelled separately. This book presents a comprehensive treatment of the state space approach to time series analysis.
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