The recursive methods are popular in time series analysis since they are computationally efficient and flexible enough to treat various changes in character of data. This paper gives a survey of the most important type of these methods including their classification and relationships existing among them. Special attention is devoted to i) robustification of some recursive methods, capable of facing outliers in time series, and ii) modifications of recursive methods for time series with missing observations
Singular spectrum analysis is a powerful non-parametric time series method that applies singular va...
Identification and estimation of outliers in time series is proposed by using empirical likelihood m...
In a modern technology generation, big volumes of data are evolved under numerous operations compare...
The recursive methods are popular in time series analysis since they are computationally efficient a...
summary:Recursive time series methods are very popular due to their numerical simplicity. Their theo...
This work presents two algorithms to estimate missing values in time series. The first is the Kalman...
This is a revised version of the 1984 book of the same name but considerably modified and enlarged t...
This paper proposed the combination of two statistical techniques for the detection and imputation o...
Available from STL Prague, CZ / NTK - National Technical LibrarySIGLECZCzech Republi
This study attempts to better understand the impact of an outlier in time series model and the impor...
One of the characteristics of almost any data collection is the presence of outstanding series and m...
This thesis is concerned with the theoretical and practical aspects of some problems in Bayesian tim...
This study is about practical forecasting and analysis of time series, to investigate the effective...
In this paper we show how the forward search, free from masking and swamping problems, can detect ma...
Singular spectrum analysis is a powerful non-parametric time series method that unfolds an observed ...
Singular spectrum analysis is a powerful non-parametric time series method that applies singular va...
Identification and estimation of outliers in time series is proposed by using empirical likelihood m...
In a modern technology generation, big volumes of data are evolved under numerous operations compare...
The recursive methods are popular in time series analysis since they are computationally efficient a...
summary:Recursive time series methods are very popular due to their numerical simplicity. Their theo...
This work presents two algorithms to estimate missing values in time series. The first is the Kalman...
This is a revised version of the 1984 book of the same name but considerably modified and enlarged t...
This paper proposed the combination of two statistical techniques for the detection and imputation o...
Available from STL Prague, CZ / NTK - National Technical LibrarySIGLECZCzech Republi
This study attempts to better understand the impact of an outlier in time series model and the impor...
One of the characteristics of almost any data collection is the presence of outstanding series and m...
This thesis is concerned with the theoretical and practical aspects of some problems in Bayesian tim...
This study is about practical forecasting and analysis of time series, to investigate the effective...
In this paper we show how the forward search, free from masking and swamping problems, can detect ma...
Singular spectrum analysis is a powerful non-parametric time series method that unfolds an observed ...
Singular spectrum analysis is a powerful non-parametric time series method that applies singular va...
Identification and estimation of outliers in time series is proposed by using empirical likelihood m...
In a modern technology generation, big volumes of data are evolved under numerous operations compare...