Detecting Outliers in Time Series Data

Chernick, Michael R.; Downing, Donald L.; Pike, D.H.

doi:10.2307/2287300

Cited by 15 publications

(12 citation statements)

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“…11-13. In the first case, the magnitude of the outlier as well as the volatility in the input series have a bandwagon effect on the autocovariance generated in (11), according to Chernick et al (1982), this may jeopardize the autocovariance function as an estimation and identification tool. Bartlett (1946) and Shangodoyin (2011) have claimed that where series (Z t , D t , Y t ) are themselves auto correlated, the lagged cross-correlation estimates can have high variance and the estimate at different lags can be highly correlated with one another, this situation can be attributed to the presence of outlier in series and masking of the volatility.…”

Section: Methodsmentioning

confidence: 99%

Specification of Dynamic Time Series Model with Volatile-Outlier Input Series

Lasisi¹,

Shangodoyin²,

Sangodoyin³

et al. 2011

American Journal of Applied Sciences

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Problem statement:This study considers the precision of the output series generated from aberrant input series in the context of the distribution of the dynamic estimate and also investigate relative merit of analyzing residuals with outliers for a volatility input dynamic model. Approach: The study developed a methodology for checking volatility at every time point and evaluates the influence of volatility and outliers on both the estimates of the fitted Dynamic Model (DM) and test criterion for model adequacy. Results: Both the analytical and empirical findings in this study reveal that outliers affect significantly the estimates of the dynamic model and there was a masking effect of volatility with outliers in the series and therefore jeopardizes test criterion for model adequacy because outlier series were embedded in its computation. Conclusion: The analysis of outlier in dynamic model specification can involve the determination of volatility, most especially in economic series for which causal relationship can proffer some evidence based solutions to decision makers on pressing economic issues. The model specified in this study has shown the influence of outlier embedded with volatility in empirical study on dynamic function modelling. In the first instance, outlier significantly affects the estimates of the model, apart from this; the model residual is affected, these have a combine effect on the precision of output generated.

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Section: Methodsmentioning

confidence: 99%

Specification of Dynamic Time Series Model with Volatile-Outlier Input Series

Lasisi¹,

Shangodoyin²,

Sangodoyin³

et al. 2011

American Journal of Applied Sciences

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show abstract

“…In our measurements, which can be outlier-contaminated time series, points corresponding to unusual water levels unduly distort the estimates of correlation coefficients. The goal here is to identify the position of such points in time series with measurements of underground water level (TSWLM), by considering the influence function for the autocorrelations p(k), as has been suggested by Chernick, Downing & Pike (1982).…”

Section: Methods Of Analysismentioning

confidence: 99%

“…(5) Since p, a, p(k) can be estimated from our data and u,,k,j are observations from independent N(0, l), the quantity IF( ) has a known distribution which has been used by Chernick et al (1982) in order to determine unusually large values in absolute terms.…”

Section: Zf[f T(f) X ]mentioning

confidence: 99%

“…The equation (3) Natural summary statistics have also been suggested by Chernick et al (1982) in order to test for outliers in time series. This statistic is defined as m A, = 1/2" z {(u;k,l)(U;k,2)[1 -P2(k)l).…”

Section: Zf[f T(f) X ]mentioning

confidence: 99%

“…. , x,Chernick et al (1982) have suggested considering an n x m matrix {IF[F, p(k), (y,, y,+k)]} where p(k) is the autocorrelation of lagk, F is the bivariate distribution for (v, YI+J, and = (XIp)/a for each t.…”

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confidence: 99%

See 2 more Smart Citations

Application of an outlier detecting method for identifying changes in the underground water level caused by crustal movements

Asteriadis

Zioutas

1990

Geophysical Journal International

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In order to detect the disturbances of the underground water level caused by a forthcoming earthquake, a graphical method has been applied for the analysis of the time series of the continuous daily measurements of the underground water level. These measurements have been done in six wells selected in the area of the recent great earthquake near Thessaloniki, Greece.The study of these measurements for the time period from December 1983 to August 1986 shows close correlation between the changes in the underground water level and the earthquakes with epicentres in this area.

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Gross-Error Sensitivies of GM and RA-Estimates

Martin

Yohai

1984

Robust and Nonlinear Time Series Analysis

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Detecting Outliers in Time Series Data

Cited by 15 publications

References 0 publications

Specification of Dynamic Time Series Model with Volatile-Outlier Input Series

Specification of Dynamic Time Series Model with Volatile-Outlier Input Series

Application of an outlier detecting method for identifying changes in the underground water level caused by crustal movements

Gross-Error Sensitivies of GM and RA-Estimates

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