Analyzing categorical time series in the presence of missing observations

This paper presents a proposal based on an evolutionary algorithm for imputing missing observations in time series. A genetic algorithm based on the minimization of an error function derived from their autocorrelation function, mean, and variance is presented. All methodological aspects of the genetic structure are presented. An extended description of the design of the fitness function is provided. Four application examples are provided and solved by using the proposed method.

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Testing for autoregressive disturbances in a time series regression with missing observations

Journal of Econometrics ◽

10.1016/0304-4076(93)90066-e ◽

1993 ◽

Vol 57 (1-3) ◽

pp. 233-255 ◽

Cited By ~ 3

Author(s):

Thomas S. Shively

Keyword(s):

Time Series ◽

Missing Observations ◽

Time Series Regression

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Nonparametric estimation of the spectral density of amplitude-modulated time series with missing observations

Statistics & Probability Letters ◽

10.1016/j.spl.2014.06.013 ◽

2014 ◽

Vol 93 ◽

pp. 7-13 ◽

Cited By ~ 2

Author(s):

Sam Efromovich

Keyword(s):

Time Series ◽

Spectral Density ◽

Nonparametric Estimation ◽

Missing Observations

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Regression Models for Categorical Time Series

Regression Models for Time Series Analysis - Wiley Series in Probability and Statistics ◽

10.1002/0471266981.ch3 ◽

2005 ◽

pp. 89-137

Keyword(s):

Time Series ◽

Regression Models ◽

Categorical Time Series

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Imputation of Missing Observations for Heavy Tailed Cyclostationary Time Series

Applied Condition Monitoring - Cyclostationarity: Theory and Methods - II ◽

10.1007/978-3-319-16330-7_9 ◽

2015 ◽

pp. 179-187 ◽

Cited By ~ 1

Author(s):

Christiana Drake ◽

Jacek Leskow ◽

Aldo M. Garay

Keyword(s):

Time Series ◽

Missing Observations ◽

Heavy Tailed

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Sieve Bootstrap With Variable-Length Markov Chains for Stationary Categorical Time Series

Journal of the American Statistical Association ◽

10.1198/016214502760046998 ◽

2002 ◽

Vol 97 (458) ◽

pp. 443-471 ◽

Cited By ~ 9

Author(s):

Peter Bühlmann

Keyword(s):

Time Series ◽

Markov Chains ◽

Variable Length ◽

Sieve Bootstrap ◽

Categorical Time Series

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Model Selection Criteria for Model-Based Clustering of Categorical Time Series Data: A Monte Carlo Study

Studies in Classification, Data Analysis, and Knowledge Organization - Advances in Data Analysis ◽

10.1007/978-3-540-70981-7_3 ◽

2007 ◽

pp. 23-30 ◽

Cited By ~ 4

Author(s):

José G. Dias

Keyword(s):

Time Series ◽

Monte Carlo ◽

Model Selection ◽

Selection Criteria ◽

Time Series Data ◽

Monte Carlo Study ◽

Series Data ◽

Model Selection Criteria ◽

Model Based Clustering ◽

Categorical Time Series

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Categorical Time Series Analysis

Wiley StatsRef: Statistics Reference Online ◽

10.1002/9781118445112.stat08132 ◽

2018 ◽

pp. 1-8

Author(s):

Christian H. Weiß

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Series Analysis ◽

Categorical Time Series

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A Bayesian Approach to Kalman Filter for Elliptically Contoured Distribution and its Application in Time Series Models

Calcutta Statistical Association Bulletin ◽

10.1177/0008068319940102 ◽

1994 ◽

Vol 44 (1-2) ◽

pp. 11-28 ◽

Cited By ~ 2

Author(s):

A. K. Basu ◽

J. K. Das

Keyword(s):

Time Series ◽

Kalman Filter ◽

Moving Average ◽

State Equation ◽

Autoregressive Moving Average ◽

Missing Observations ◽

Estimation Of Parameters ◽

Bayesian Formulation ◽

Set Up ◽

Elliptically Contoured

This paper develops a Bayesian formulation of Kalman filter under the errors having elliptically contoured distributions in both observation equation and system (or state) equation, using some recent results in multivariate analysis. Estimation of parameters in case of missing observations and prediction of missing observations as well are dealt with under the above set up of autoregressive-moving average process in time series. Two illustrative examples are presented with the help of AR(1) model and ARMA (1, 1) model.

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