• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.395-406
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• 2022

This paper studies the outlier detection method for multivariate long memory time series. The existing outlier detection methods are based on a short memory VARMA model, so they are not suitable for multivariate long memory time series. It is because higher order of autoregressive model is necessary to account for long memory, however, it can also induce estimation instability as the number of parameter increases. To resolve this issue, we propose outlier detection methods based on the VHAR structure. We also adapt the robust estimation method to estimate VHAR coefficients more efficiently. Our simulation results show that our proposed method performs well in detecting outliers in multivariate long memory time series. Empirical analysis with stock index shows RVHAR model finds additional outliers that existing model does not detect.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.103-111
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• 2023

This paper proposes a new data correction technique that transforms anomalies in time series data into normal values. With the recent development of IT technology, a vast amount of time-series data is being collected through sensors. However, due to sensor failures and abnormal environments, most of time-series data contain a lot of anomalies. If we build a predictive model using original data containing anomalies as it is, we cannot expect highly reliable predictive performance. Therefore, we utilizes the LSTM-GAN model to detect anomalies in the original time series data, and combines DTW (Dynamic Time Warping) and GAN techniques to replace the anomaly data with normal data in partitioned window units. The basic idea is to construct a GAN model serially by applying the statistical information of the window with normal distribution data adjacent to the window containing the detected anomalies to the DTW so as to generate normal time-series data. Through experiments using open NAB data, we empirically prove that our proposed method outperforms the conventional two correction methods.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.1
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• pp.58-63
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• 2010

In this paper, we propose a simple technique for the detection of a frequency jump in the GPS clock behavior. GPS satellite atomic clocks have characteristics of a second order polynomial in the long term and a non-periodic frequency drift in the short term, showing a sudden frequency jump occasionally. As satellite clock anomalies influence on GPS measurements, it requires to develop a real time technique for the detection of the clock anomaly on the real-time GPS precise point positioning. The proposed technique is based on Teager energy which is mainly used in the field of various signal processing for the detection of a specific signal or symptom. Therefore, we employed the Teager energy for the detection of the jump phenomenon of GPS satellite atomic clocks, and it showed that the proposed clock anomaly detection strategy outperforms a conventional detection methodology.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2016.11a
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• pp.255-256
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• 2016

우리나라의 2015년 노인 인구는 전체 인구의 13.1%를 차지하고 2015년 경찰청 교통사고통계에 의하면 65세 이상 노인의 교통사고 사망률은 전체 교통사고 사망률의 약 2.57배 높은 것으로 나타났다. 본 연구에서는 노인 운전자와 성인 운전자의 사망사고에 대한 시계열 모형을 확인하고 추세에 큰 차이가 있는지 확인하고자 하였다. 분석방법인 시계열분석은 단기예측에 신뢰성이 더 높은 것으로 알려져 있다. ARIMA 모형으로 시계열분석을 하기 위해서는 최소 50~60개 이상의 관측값이 필요하며 따라서 본 연구에서는 인천광역시를 대상으로 2010년부터 2015년까지 6년간의 교통사고 데이터를 노인 운전자와 성인 운전자로 구분하고 사망사고에 대한 시계열 모형을 확인하였다.

• 한국ITS학회:학술대회논문집
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• 2003.11a
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• pp.297-302
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• 2003

• The Korean Journal of Applied Statistics
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• v.9 no.1
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• pp.125-137
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• 1996

Time series data are often contaminated with outliers due to influence of unusal and non-responsitive events. The effect of the outliers is larger in the time series analysis than in the other statistical analysis, because the time series data have dependent structure over time. This paper focuses on the effect of patchy outliers on forecasting. Especially, the increase of the mean square of the l-step-ahead forecast error is derived and used to evaluate the impact of those outliers on the forecast. We fine, in general, that this increase is rather small, provided that the patchy outliers does not occur too close to the forecast origin.

• Proceedings of the Korean Institute of Industrial Safety Conference
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• 2001.11a
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• pp.25-30
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• 2001

엔진이나 콤프레셔 등과 같은 기기 진동의 동특성은 불규칙적으로 변동하는 성분을 갖는 랜덤 프로세스로 그것을 수학적으로 명확히 기술하는 것은 어렵다/sup 1)/. 하지만 통계학적인 입장에서 시계열 데이터를 보면 시계열 데이터가 확률변수로서 각각의 모집단에 속한다. 따라서 이 점에 주목하여 시계열의 확률적인 특징을 추출하는 것으로, 각각의 시계열 데이터를 확률공간으로 구별하는 것이 가능하다면 시계열 데이터에 의해 표현되어진 상태의 식별가능하다는 이론이 성립된다/sup 2)/.(중략)

• The Korean Journal of Applied Statistics
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• v.23 no.1
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• pp.41-47
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• 2010

In this paper, we show the performance evaluation of outlier detection methods based on the GARCH model. We first introduce GARCH model and the methods of outlier detection in the GARCH model. The results of small simulation and the real KOSPI data show the out-performance of the outlier detection method over the traditional method in the GARCH model.

• Journal of the Korean GEO-environmental Society
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• v.19 no.1
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• pp.5-14
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• 2018

All kinds of monitoring data in construction site could have outlier created from diverse cause. In this study generation technique of synthesis value, its regression, final outlier detection and assessment are conducted to distinct outlier data included in extensive time series dataset. Synthesis value having weight factor of correlation between a number of datasets consist of many monitoring data enable to detect outlier by increasing its correlation. Standard artificial dataset in which intentional outliers are inserted has been used for assessment of synthesis value technique. These results showed increase of detection accuracy for outlier and general tendency in case of having different time series models in common. Accuracy of outlier detection increased in case of using more dataset and showing similar time series pattern.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.627-637
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• 2005

Small area estimation has been studied using various methods such as direct, indirect, synthetic and based on regression or time series model . In this paper we investigate a motel-based small area estimation which takes into account the spare time autoregressive model. The Economic Active Population Surveys in 2001 are used for analysis and the results from space-time autoregressive(STAR) and simultaneous autoregressive(SAR) model are compared with using MSE, MAE and MB.