A Strategy to Complete a Time Series with Missing Observations

1984 ◽  
pp. 251-275 ◽  
Author(s):  
Robert B. Miller ◽  
Osvaldo Ferreiro
Keyword(s):  
Time Series ◽  
Missing Observations

AN EVOLUTIONARY APPROACH FOR IMPUTING MISSING DATA IN TIME SERIES

2010 ◽  
Vol 19 (01) ◽  
pp. 107-121 ◽  
Author(s):  
JUAN CARLOS FIGUEROA GARCÍA ◽  
DUSKO KALENATIC ◽  
CESAR AMILCAR LÓPEZ BELLO
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Missing Data ◽  
Genetic Structure ◽  
Evolutionary Algorithm ◽  
Fitness Function ◽  
Error Function ◽  
Missing Observations ◽  
Mean And Variance ◽  
Methodological Aspects

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.

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

1994 ◽  
Vol 44 (1-2) ◽  
pp. 11-28 ◽  
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.

DETECTION AND MODELING OF REGRESSION PARAMETER VARIATION ACROSS FREQUENCIES

1999 ◽  
Vol 3 (1) ◽  
pp. 69-83 ◽  
Author(s):  
Hui Boon Tan ◽  
Richard Ashley
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Time Domain ◽  
Simple Technique ◽  
Parameter Variation ◽  
Missing Observations ◽  
Regression Parameter ◽  
Time Series Regression ◽  
Parameter Instability ◽  
The Time Domain

A simple technique for directly testing the parameters of a time-series regression model for instability across frequencies is presented. The method can be implemented easily in the time domain, so that parameter instability across frequency bands can be conveniently detected and modeled in conjunction with other econometric features of the problem at hand, such as simultaneity, cointegration, missing observations, and cross-equation restrictions. The usefulness of the new technique is illustrated with an application to a cointegrated consumption-income regression model, yielding a straightforward test of the permanent income hypothesis.

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