Filtering and smoothing algorithms for state space models
نویسندگان
چکیده
منابع مشابه
Smoothing Algorithms for State-Space Models
A prevalent problem in statistical signal processing, applied statistics, and time series analysis is the calculation of the smoothed posterior distribution, which describes the uncertainty associated with a state, or a sequence of states, conditional on data from the past, the present, and the future. The aim of this paper is to provide a rigorous foundation for the calculation, or approximati...
متن کاملPrediction Intervals for Exponential Smoothing State Space Models
The main objective of this paper is to provide analytical expressions for forecast variances that can be used in prediction intervals for the exponential smoothing methods. These expressions are based on state space models with a single source of error that underlie the exponential smoothing methods. Three general classes of the state space models are presented. The first class is the standard ...
متن کاملForecasting based on state space models for exponential smoothing
In business, there is a frequent need for fully automatic forecasting that takes into account trend, seasonality and other features of the data without need for human intervention. In supply chain management, for example, forecasts of demand are required on a regular basis for very large numbers of time series, so that inventory levels can be planned to provide an acceptable level of service to...
متن کاملSimulation smoothing for state-space models: A computational efficiency analysis
Simulation smoothing involves drawing state variables (or innovations) in discrete time state-space models from their conditional distribution given parameters and observations. Gaussian simulation smoothing is of particular interest, not only for the direct analysis of Gaussian linear models, but also for the indirect analysis of more general models. Several methods for Gaussian simulation smo...
متن کاملFixed-Interval Kalman Smoothing Algorithms in Singular State-Space Systems
Fixed-interval Bayesian smoothing in state-space systems has been addressed for a long time. However, as far as the measurement noise is concerned, only two cases have been addressed so far : the regular case, i.e. with positive definite covariance matrix; and the perfect measurement case, i.e. with zero measurement noise. In this paper we address the smoothing problem in the intermediate case ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computers & Mathematics with Applications
سال: 1989
ISSN: 0898-1221
DOI: 10.1016/0898-1221(89)90104-1