• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1123-1135
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• 2011

This paper considers the multivariate integrated process control procedure for detecting special causes in a multivariate IMA(1, 1) process. When the multivariate control chart signals, the special cause will be detected and eliminated from the process. However, when the elimination of the special cause costs high or is not practically possible, an alternative action is to readjust the process with approximately modified adjustment scheme. In this paper, we propose the readjustment procedure after having a true signal, and show that the use of the readjustment can reduce the deviation of a process from the target.

• Journal of Korea Water Resources Association
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• v.39 no.7 s.168
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• pp.617-626
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• 2006

In this study 3-month SPI data from 59 stations over the Korean peninsula are analyzed by deriving and spatially characterizing the EOFs. Also, the coefficient time series of EOF are applied to the multi-variate time series model to generate the time series of 10,000 years, to average them to estimate the areal average, and to decide the maximum drought severity for given return periods. Finally, the drought prevention ability is evaluated by considering the effective storage of dam within the basin and the size of agricultural area. Especially for the return period of 30 years, only the Han river basin has the potential to overcome the drought. Other river basins like the Youngsan river basin, which has a large portion of agricultural area but less water storage, are found to be very vulnerable to the rainfall-sensitive agricultural drought.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1512-1516
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• 2007

자신의 현재와 과거의 시계열데이터만을 가지고 시계열 모형을 구축하는 단변량 ARIMA모형 분석법과는 달리, 관심의 대상이 되는 출력시계열과 이와 관련있는 입력시계열의 동태적 특성을 나타내는 전이함수모형(Transfer function model)을 사용하여 소양강댐, 충주댐, 화천댐에 대한 월별 수문자료를 이용하여 유입량을 예측해 보고자 한다. 본 연구의 주요 목적은 다변량 추계학적 시스템의 해석을 위한 모형의 추정과 등정을 위한 과정을 개발하는데 있다. 일반적 추계학적 시스템 모형이 표현되며 그것으로부터 수문학적 시스템의 모형을 매우 적절하게 유도하기 위한 다중 입력-단일 출력 TF, TFN모형을 유도하는데 있다. 이 모형은 수문학적 시스템을 위한 경우에 있어 상관된 입력을 설명할 수 있도록 개발된다. 일반적으로 모형을 만드는 전략이 유도되며 실제유역시스템에 적용하여 검토된다. 한강수계 주요 다목적댐인 소양강댐, 충주댐, 화천댐의 수문자료를 가지고 추계학적 모형(TF, TFN)에 의한 결과와 실제유입량을 비교하여 검토하고자 한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.210-212
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• 2022

In the manufacturing industry, various sensors are attached to monitor the status of production facility. In many cases, the data obtained through these sensors is time series data. In order to determine whether the status of the production facility is abnormal, the process of extracting patterns from time series data must be preceded. Also various methods for extracting patterns from time series data are studied. In this paper, we use matrix profile algorithm to extract patterns from the collected multivariate time series data. Through this, the pattern of multi sensor data currently being collected from the CNC machine is extracted.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.9-9
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• 2016

많은 연구들에서 단변량 수문 변량들에 대한 불확실성 분석이 이루어지고 있지만, 다변량에 대한 불확실성에 관한 연구는 아직까지 정확하게 이루어지고 있지 않은 실정이다. 이에 본 연구에서는 갈수기(12월~4월)의 강수, 온도와 남방진동(El Ni?o-Southern Oscillation, ENSO)과 같은 수문기상학적 변량들 사이의 시간에 따른 변동 구조를 조사하고, 식별된 패턴을 이용한 강우와 온도의 예측 향상 가능성을 살펴보았다. 수문기상학적 변수간의 시변성 구조를 이해하기 위해서 각각의 단변량 매개변수와 시간에 따라 변화하는 Copula 매개변수를 동시에 추정할 수 있는 Copula 함수 기반의 새로운 다변량 비정상성 모델을 개발하고자 한다. 강우와 온도의 비정상정 단변량 분포를 생성하기 위해 ENSO 지표 또는 시계열 예측인자와 함께 시변성 모델을 적용할 수 있다. 최종적으로, 확인된 시간 변동적인 구조와 연관된 종관 패턴을 나타내고 논의하고자 한다.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.2
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• pp.81-86
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• 2022

Given that time series are used in various fields, such as finance, IoT, and manufacturing, data analytical methods for accurate time-series forecasting can serve to increase operational efficiency. Among time-series analysis methods, multi-horizon forecasting provides a better understanding of data because it can extract meaningful statistics and other characteristics of the entire time-series. Furthermore, time-series data with exogenous information can be accurately predicted by using multi-horizon forecasting methods. However, traditional deep learning-based models for time-series do not account for the heterogeneity of inputs. We proposed an improved time-series predicting method, called the temporal fusion transformer method, which combines multi-horizon forecasting with interpretable insights into temporal dynamics. Various real-world data such as stock prices, fine dust concentrates and electricity consumption were considered in experiments. Experimental results showed that our temporal fusion transformer method has better time-series forecasting performance than existing models.

• KDI Journal of Economic Policy
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• v.14 no.1
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• pp.121-145
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• 1992

This paper examines characteristics of time series data related to the construction investment(stationarity and time series components such as secular trend, cyclical fluctuation, seasonal variation, and random change) and surveys predictibility, fitness, and explicability of independent variables of various models to build a short-term construction investment forecasting model suitable for current economic circumstances. Unit root test, autocorrelation coefficient and spectral density function analysis show that related time series data do not have unit roots, fluctuate cyclically, and are largely explicated by lagged variables. Moreover it is very important for the short-term construction investment forecasting to grasp time lag relation between construction investment series and leading indicators such as building construction permits and value of construction orders received. In chapter 3, we explicate 7 forecasting models; Univariate time series model (ARIMA and multiplicative linear trend model), multivariate time series model using leading indicators (1st order autoregressive model, vector autoregressive model and error correction model) and multivariate time series model using National Accounts data (simple reduced form model disconnected from simultaneous macroeconomic model and VAR model). These models are examined by 4 statistical tools that are average absolute error, root mean square error, adjusted coefficient of determination, and Durbin-Watson statistic. This analysis proves two facts. First, multivariate models are more suitable than univariate models in the point that forecasting error of multivariate models tend to decrease in contrast to the case of latter. Second, VAR model is superior than any other multivariate models; average absolute prediction error and root mean square error of VAR model are quitely low and adjusted coefficient of determination is higher. This conclusion is reasonable when we consider current construction investment has sustained overheating growth more than secular trend.

• 한국데이터정보과학회:학술대회논문집
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• 2006.11a
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• pp.29-36
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• 2006

최근 유해성이 강한 지표오존농도가 대기환경의 주요한 문제로 부각되고 있다. 본 연구에서는 경기도 평택과 서울 정동지역의 오존농도를 설명 변수를 사용할 수 있는 다변량 시계열 모형인 ARE(자기회귀오차) 모형으로 분석하였다. ARE모형에서는 오존 전체자료를 사용한 전체모형과 오존농도가 41ppb 이상 되는 자료를 사용한 부분모형 두 가지 모형을 비교하였다. ARE의 오존농도 설명변수로는 오존농도와 연관 있는 8종류의 기상자료와 4종류의 대기오염자료를 고려하였다. 기상자료의 8가지 설명변수로 일 최고온도, 일사량, 풍속, 상대습도, 강수량, 이슬점온도, 수증기압, 운량 자료를 사용하였다. 대기오염자료의 4가지 설명변수로는 아황산가스(SO2), 이산화질소(NO2), 코발트(CO)와 프로메툼 10(PM10)를 사용하였다.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.61-73
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• 2016

We consider the wild bootstrap Ljung-Box (LB) test for autocorrelation in residuals of fitted multivariate time series models. The asymptotic chi-square distribution under the IID assumption is traditionally used for the LB test; however, size distortion tends to occur in the usage of the LB test, due to the conditional heteroskedasticity of financial time series. In order to overcome such defects, we propose the wild bootstrap LB test for autocorrelation in residuals of fitted vector autoregressive and error correction models. The simulation study and real data analysis are conducted for finite sample performance.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1275-1283
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• 2013

This paper considers the multivariate integrated process control procedure for detecting special causes in a multivariate IMA(1, 1) process. When the multivariate control chart signals, the special cause will be detected and eliminated from the process. However, when the elimination of the special cause costs high or is not practically possible, an alternative action is to readjust the process with approximately modified adjustment scheme. In this paper, we estimate parameters in the readjustment procedure after having a true signal in the multivariate integrated process control.