• Communications for Statistical Applications and Methods
• /
• v.17 no.3
• /
• pp.377-390
• /
• 2010

In the case of time-series analysis, it is often more convenient to rely on the frequency domain than the time domain. Spectral density is the core of the frequency-domain analysis that describes autocorrelation structures in a time-series process. Possible ways to estimate spectral density are to compute a periodogram or to average the periodogram over some frequencies with (un)equal weights. This can be an attractive tool to measure the similarity between time-series processes. We employ the metrics based on a smoothed periodogram proposed by Park and Kim (2008) for the classification of different classes of time-series processes. We consider several lag windows with unequal weights instead of a modified Daniel's window used in Park and Kim (2008). We evaluate the performance under various simulation scenarios. Simulation results reveal that the metrics used in this study split the time series into the preassigned clusters better than do the raw-periodogram based ones proposed by Caiado et al. 2006. Our metrics are applied to an economic time-series dataset.

• Safety and Health at Work
• /
• v.3 no.4
• /
• pp.294-297
• /
• 2012

Previous studies on the vast increase in suicide mortality in Southeast Asia have indicated that suicide rates increase in parallel with a rise in unemployment or during periods of economic recession. This paper examines the effects of economic recession on suicidal rates amongst agriculture, fisheries, and forestry workers in Korea. Monthly time-series gross domestic product (GDP) data were linked with suicidal rates gathered from the cause of death records between1993-2008. Data were analyzed using generalized additive models to analyze trends, while a polynomial lag model was used to assess the unconstrained time lag effects of changes in GDP on suicidal rate. We found that there were significant inverse correlations between changes in GDP and suicide for a time lag of one to four months after the occurrence of economic event. Furthermore, it was evident that the overall relative risks of suicide were high enough to bring about social concern.

• Journal of the Korean Society of Propulsion Engineers
• /
• v.16 no.1
• /
• pp.1-8
• /
• 2012

Acoustic-pressure response of diluted hydrogen-air diffusion flames is investigated numerically by adopting a fully unsteady analysis of flame structures in low and high pressure regimes. As acoustic frequency increases, finite-rate chemistry is enhanced through a nonlinear accumulation of heat release rate for any pressure regime, leading to a high amplification index. Same numerical results are analyzed with application of a pressure-sensitive time lag model, and thereby, interaction index and time lag are calculated for each pressure regime. The interaction index has the largest value in each pressure regime at an acoustic frequency near 1000 Hz. In a high-pressure regime, flames are more unstable than in a low-pressure regime. The interaction index shows a good agreement with the amplification index.

• Journal of Environmental Impact Assessment
• /
• v.14 no.2
• /
• pp.55-62
• /
• 2005

This study applied the Neural Network and Fuzzy theory to show water-purity control and preventive measure in water quality forecasting of the future river. This study picked out NAJU and HAMPYUNG as the subject of investigation and used monthly the water quality and the outflow data of KWANGJU2, NAJU, YOUNGSANNPO and HAMPYUNG from 1995 to 1999 to forecast BOD, COD, T-N, T-P water density. The datum from 1995 to 1999 are used for study and that of 2000 are used for verification. To develop model of water quality forecasting, firstly, this research formed Neural Network model and divided Neural Network model into two case - the case of considering lag and not considering. And this study selected optimal Neural Network model through changing the number of hidden layer based on input layer(n) from n to 3n. Through forecasting result, the case without considering lag showed more precise simulated result. Accordingly, this study intended to compare, analyse that Fuzzy model using the method without considering lag with Neural Network model. As a result, this study found that the model without considering lag in Neural Network Network shows the most excellent outcome. Thus this study examined a forecasting accuracy, analyzed result and verified propriety through appling the method of water quality forecasting using Neural Network and Fuzzy Algorithms to the actual case.

• The Korean Journal of Applied Statistics
• /
• v.16 no.1
• /
• pp.87-100
• /
• 2003

Most of statistical data are generated by a set of dynamic, stochastic, and simultaneous relations. An important question is how to specify statistical models so that they are consistent with the dynamic feature of those data. A general hypothesis is that the lagged effect of a change in an explanatory variable is not felt all at once at a single point in time, but The impact is distributed over a number of future points in time. In other words, current control variables are determined by a function that can be reduced to a distributed lag function of past observations. It is possible to explain the relationship between variables in different points of time and to estimate the long-run impacts of a change in a variable on another if time lag series of explanatory variables are incorporated in the model specification. In this study, distributed lag structure is applied to the domestic stock market model to capture the dynamic response of the market by exogenous shocks. The Domestic market is found more responsive to the changes in foreign market factors both in the short and the long run.

• Journal of Korean Society of Water and Wastewater
• /
• v.35 no.6
• /
• pp.417-424
• /
• 2021

Algal bloom is an ongoing issue in the management of freshwater systems for drinking water supply, and the chlorophyll-a concentration is commonly used to represent the status of algal bloom. Thus, the prediction of chlorophyll-a concentration is essential for the proper management of water quality. However, the chlorophyll-a concentration is affected by various water quality and environmental factors, so the prediction of its concentration is not an easy task. In recent years, many advanced machine learning algorithms have increasingly been used for the development of surrogate models to prediction the chlorophyll-a concentration in freshwater systems such as rivers or reservoirs. This study used a light gradient boosting machine(LightGBM), a gradient boosting decision tree algorithm, to develop an ensemble machine learning model to predict chlorophyll-a concentration. The field water quality data observed at Daecheong Lake, obtained from the real-time water information system in Korea, were used for the development of the model. The data include temperature, pH, electric conductivity, dissolved oxygen, total organic carbon, total nitrogen, total phosphorus, and chlorophyll-a. First, a LightGBM model was developed to predict the chlorophyll-a concentration by using the other seven items as independent input variables. Second, the time-lagged values of all the input variables were added as input variables to understand the effect of time lag of input variables on model performance. The time lag (i) ranges from 1 to 50 days. The model performance was evaluated using three indices, root mean squared error-observation standard deviation ration (RSR), Nash-Sutcliffe coefficient of efficiency (NSE) and mean absolute error (MAE). The model showed the best performance by adding a dataset with a one-day time lag (i=1) where RSR, NSE, and MAE were 0.359, 0.871 and 1.510, respectively. The improvement of model performance was observed when a dataset with a time lag up of about 15 days (i=15) was added.

• Journal of Korea Water Resources Association
• /
• v.37 no.12
• /
• pp.1065-1074
• /
• 2004

This study aims at the introduction of new catchment shape descriptor, developed by Moussa(2003), based on equivalent ellipse and the assessment of its hydrologic applicability. Two descriptors a+b and a+b+${\varepsilon}OM$were correlated to the lag time and those were applied to the estimation of representative values of Nash model parameters. They are applied in order to examine the practicality to 3 catchments in Korea, catchments in Korea, respectively, i.e. Pyeongchanggang catchment in Han river, Bocheongcheon catchment in Geum river and Wicheon catchment in Nakdong river. As a result both of two descriptors show higher correlations to the lag lime than classical geomorphologic factors and hereby Moussa's suggestion(2003) is confirmed. For the sake of simplicity the former is recommended. Also representative IUHs derived from this study show consistent basin response characteristics. It is desirable to conduct further more case studies on many other basins.

• Korea Trade Review
• /
• v.44 no.2
• /
• pp.127-141
• /
• 2019

In this study, the relationship between Baltic Dry Index(BDI) and maritime trade volume in the dry cargo market was verified using the vector autoregressive (VAR) model. Data was analyzed from 1992 to 2018 for iron ore, steam coal, coking coal, grain, and minor bulks of maritime trade volume and BDI. Granger causality analysis showed that the BDI affects the trade volume of coking coal and minor bulks but the trade volume of iron ore, steam coal and grain do not correlate with the BDI freight index. Impulse response analysis showed that the shock of BDI had the greatest impact on coking coal at the two years lag and the impact was negligible at the ten years lag. In addition, the shock of BDI on minor cargoes was strongest at the three years lag, and were negligible at the ten years lag. This study examined the relationship between maritime trade volume and BDI in the dry bulk shipping market in which uncertainty is high. As a result of this study, there is an economic aspect of sustainability that has helped the risk management of shipping companies. In addition, it is significant from an academic point of view that the long-term relationship between the two time series was analyzed through the causality test between variables. However, it is necessary to develop a forecasting model that will help decision makers in maritime markets using more sophisticated methods such as the Bayesian VAR model.

• Journal of Environmental Science International
• /
• v.15 no.4
• /
• pp.365-372
• /
• 2006

In this study investigated that topographical parametersestimate and calculated travel time, storage coefficient and lag time by watershed dividing 11, 8, 6 and 2. The results showed the more divide watershed, the more increase peak discharges. The results showed that Kraven-Clark-Kraven case is good simulated by compared observed data with calculated data. The sub-basin number are adequate $6{\sim}11$ for wichun and travel times compare observed data with calculated data at the younggok, to take about $18{\sim}20hr$ by simulated results but observed data shorter $8{\sim}10hr$. From this study results showed that it could be make narrow parameter estimate for observed hydrograph simulation, if more observed velocity and hydrograph. Also, as results of this study that is help to estimate parameters (arrival time, storage coefficient and lag time for Clark model.

• IE interfaces
• /
• v.19 no.4
• /
• pp.281-290
• /
• 2006

The main objective of this study is to analyze the structure of efficiency of R&D input variables and the attributes of patent information as output of R&D activities in the major manufacturing industries (electric & electronics, machinery, chemical, textile) from the mid-1970s to the late-1990s by the development of "mapping technique". To attain this objective we first have examined the attribute of time-lag which depends on the absolute, and the cumulative values between input and output. And on the basis of this result, we have made an analysis on the impact to extract the main variables affecting patent by industries. Moreover, according to time trend of the impact variables, we have analyzed the structure of R&D efficiency, and of technological progress which will be changed with time by patent information. It has been aimed at constructing technological progress patterns in the Korea industry.