Detection of Outliers in Time Series Data: A Frequency Domain Approach

Shittu, Olanrewaju I.; Shangodoyin, D. K.

doi:10.3923/ajsr.2008.130.137

Cited by 10 publications

(2 citation statements)

References 4 publications

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“…Applying spectral methods to identify anomalies in the frequency domain is suitable, since outliers often cause a phase and amplitude shift in the Fourier series. An algorithm was proposed by Shittu and Shangodoyin that makes use of Maximum Likelihood Estimation (MLE) to approximate the parameters of a Fourier model in order to determine the variance between the approximation and the actual values [29]. However, it was found that the algorithm performed well for three datasets, but very poorly for two others.…”

Section: E Mean Absolute Spectral Deviationmentioning

confidence: 99%

Predictive Noise Detection in Gramophone Records

Stallmann

Engelbrecht

2016

IJSPS

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Gramophones have regained widespread popularity over the past few years. Being an analogue storage medium, gramophone records are subject to distortions which are mainly caused by scratches. This paper empirically analyses various outlier detection algorithms and proposes a novel predictive approach for noise detection. Twelve different forecasting models were utilized for the predictive deviation method. Once outliers are identified, they can be reconstructed using interpolation algorithms or time series approximation. Experiments were conducted on 800 songs from eight genres, both with artificial and real gramophone noise. The algorithms were compared according to their detection rate, computational speed and the tradeoff between accuracy and speed. It was found that the novel absolute predictive deviation using the autoregressive integrate moving average model performed best overall. The experiments also indicated that it was easier to detect noise in stable signals from genres, compared to noise in volatile signals.

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Section: E Mean Absolute Spectral Deviationmentioning

confidence: 99%

Predictive Noise Detection in Gramophone Records

Stallmann

Engelbrecht

2016

IJSPS

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“…The technique was able to detect Additive and Innovative outlier simultaneously". Outliers in Multivariate Time Series Shittu and Sangodoyin [40], considered the identification of outliers in frequency domain using the spectral method.…”

Section: Introductionmentioning

confidence: 99%

Performance of Two Generating Mechanisms in Detection of Outliers in Multivariate Time Series

Oluyomi.¹

2016

AJTAS

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This work is focused on developing two outlier generating mechanisms for the detection of outliers in the multivariate time series setting that is capable of ameliorating the swamping effect on regular observations in time series data. Specifying two-variable Vector Autoregressive (VAR) models and assuming innovative and multiplicative effect of outliers on time series data, the magnitude and variance of outlier were derived for the generating models by method of least squares. A modified test statistics were also developed to detect single outliers both in the response and explanatory variables. Real and simulated data were used to establish the validity of the models. The results show that the multiplicative is better than the additive model in terms of the number of outliers detected and the residual variance. This result is in line with previous studies in outlier detection in univariate time series.

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