• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.205-223
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• 2021

In this paper, we study a method to determine the existence of unit roots by using the adaptive lasso. The previously proposed method that applied the adaptive lasso to the original time series has low power when there is an unknown trend. Therefore, we propose a modified version that fits the ADF regression model without deterministic component using the adaptive lasso to the detrended series instead of the original series. Our Monte Carlo simulation experiments show that the modified method improves the power over the original method and works well in large samples.

• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.573-589
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• 2023

In a piecewise linear trend model, the change points coincide with the mean change points of the first differenced time series. Therefore, by detecting the mean change points of the first differenced time series, one can estimate the change points of the piecewise linear trend model. In this paper, based on this fact, a method is proposed for detecting change points of the piecewise linear trend model using the simple moving average of the first differenced time series rather than estimates of the slope or residuals. Our Monte Carlo simulation experiments show that the proposed method performs well in estimating the number of change points not only when the error terms in the piecewise linear trend model are independent but also when they are serially correlated.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.723-738
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• 2020

In this paper, we study a method to discriminate between trend stationary and difference stationary processes. Since a crucial ingredient of this discrimination is to determine the existence of unit root, we can use a unit root testing strategy. So, we introduce a discrimination based on unit root testing and propose the method using the adaptive lasso. Our Monte Carlo simulation experiments show that the adaptive lasso improves the discrimination accuracy when the process is trend stationary, but has lower accuracy than unit root strategy where the process is difference stationary.

• Annual Conference of KIPS
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• 2018.10a
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• pp.398-402
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• 2018

비정상(non-stationary) 장기 시계열 안에서도, 단기적으로 추세의 변화가 일시적인 것인지, 아니면 구조적으로 변한 것인지를 적시에 판단하는 것은 중요하다. 이는 시계열 추세의 변화를 상시 감지하여, 변화에 맞는 적정한 수준의 대응을 할 필요가 있기 때문이다. 본 연구에서는 장기 시계열이 주어진 상황에서, 단위근 검정법을 기반으로 단기적으로 구조변화를 감지하여, 이러한 변화가 얼마나 지속될 것인지를 시각적으로 판단할 수 있는 방법을 제시하고자 한다.

• 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.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.525-540
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• 2000

Characteristics of precipitation in Seoul have been examined by using long-term observational data. Precipitation records from modern rain gauges were used for 1908-1996, together with the traditional Korean rain gauge (called Chukwookee) observations for 1777-1907. A linear trend analysis of seasonal total rainfall shows no significant trends over the last 200 years A wavelet transform analysis was performed to figure out the transient variations of precipitation.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.9
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• pp.377-384
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• 2015

In this paper we propose a visualization tool for distortion-free time-series matching. Supporting distortion-free is a very important factor in time-series matching to get more accurate matching results. In this paper, we visualize the result of time-series matching, which removes various time-series distortions such as noise, offset translation, amplitude scaling, and linear trend by using moving average, normalization, linear detrending transformations, respectively. The proposed visualization tool works as a client-server model. The client sends a user-selected time-series, of which distortions are removed, to the server and visualizes the matching results. The server efficiently performs the distortion-free time-series matching on the multi-dimensional R*-tree index. By visualizing the matching result as five different charts, we can more easily and more intuitively understand the matching result.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.7
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• pp.289-302
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• 2019

Structural change of time series means that the distribution of observations is relatively stable in the period of constituting the entire time series data, but shows a sudden change of the distribution characteristic at a specific time point. Within a non-stationary long-term time series, it is important to determine in a timely manner whether the change in short-term trends is transient or structurally changed. This is because it is necessary to always detect the change of the time series trend and to take appropriate measures to cope with the change. In this paper, we propose a method for decision makers to easily grasp the structural changes of time series by visualizing the test results based on the unit root test. Particularly, it is possible to grasp the short-term structural changes even in the long-term time series through the method of dividing the time series and testing it.

• Journal of the Korean Society for information Management
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• v.36 no.4
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• pp.83-105
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• 2019

This study aims to suggest an effective method for the automatic classification of keywords with similar patterns by calculating pattern similarity of temporal data. For this, large scale news on the Web were collected and time series data composed of 120 time segments were built. To make training data set for the performance test of the proposed model, 440 representative keywords were manually classified according to 8 types of trend. This study introduces a Dynamic Time Warping(DTW) method which have been commonly used in the field of time series analytics, and proposes an application model, MA-DTW based on a Moving Average(MA) method which gives a good explanation on a tendency of trend curve. As a result of the automatic classification by a k-Nearest Neighbor(kNN) algorithm, Euclidean Distance(ED) and DTW showed 48.2% and 66.6% of maximum micro-averaged F1 score respectively, whereas the proposed model represented 74.3% of the best micro-averaged F1 score. In all respect of the comprehensive experiments, the suggested model outperformed the methods of ED and DTW.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.155-167
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• 2014

This study uses a continuous autoregressive (CAR) model to analyze daily average temperature in six Korean metropolitan cities. Data period is Jan. 1, 1954 to Dec. 31, 2010 covering 57 years. Using a relative long time series reveals that the linear time trend components are all statistically significant in the six cities, which was not shown in previous studies. Particularly the plus sign of its coefficient implies the effect on Korea of the global warming. Unit-root test results are that the temperature time series are stationary without unit-root. It turns out that CAR(3) is suitable for stochastic component of the daily temperature. Since developing suitable continuous stochastic model of the underlying weather related variables is crucial in pricing the weather derivatives, the results in this study will likely prove useful in further future studies on pricing weather derivatives.