• Journal of Korean Society of Transportation
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• v.27 no.2
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• pp.207-214
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• 2009

This study proposed an advanced warning information system based on real-time traffic conflict analysis. An algorithm to detect and analyze unsafe traffic events associated with car-following and lane-changes using individual vehicle trajectories was developed. A positive guidance procedure was adopted to provide warning information to alert drivers to hazardous traffic conditions derived from the outcomes of the algorithm. In addition, autoregressive integrated moving average (ARIMA) analyses were conducted to investigate the predictability of warning information for the enhancement of information reliability.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.735-743
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• 2011

This study analyzes inbound and outbound Korea tourism data through an intervention model. For the analysis, we adopt three intervention factors: (1) IMF bailout crisis in December 1997, (2) Severe Acute Respiratory Syndrome(SARS) outbreak in March 2003, and (3) Lehman Brothers bankruptcy in September 2008. The empirical results show that only the SARS factor lowered inbound tourism from April 2003 with a drastic decline in May 2003 and gradually decaying since then. However, all three factors significantly lowered tourism in the case of outbound tourism. Especially, the effect of the IMF is shown to be permanent from December 1997 and the effects of SARS and the Lehman Brothers bankruptcy abrupt and temporary with a gradual decay.

• The Journal of Bigdata
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• v.4 no.2
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• pp.105-114
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• 2019

Missing value replacement is one of the big issues in data analysis. If you ignore the occurrence of the missing value and proceed with the analysis, a bias can occur and give incorrect results for the estimate. In this paper, we need to find and apply an appropriate alternative to missing data from weather data. Through this, we attempted to clarify and compare the simulations for various situations using existing methods such as MICE and MissForest based on R and time series-based models. When comparing these results with each variable, it was determined that the kalman filter of the auto arima model using the ImputeTS package and the MissForest model gave good results in the weather data.

• Journal of applied mathematics & informatics
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• v.5 no.2
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• pp.373-382
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• 1998

Most of the works in Time Series Analysis are based on the Auto Regressive Integrated Moving Average (ARIMA) models presented by Box and Jeckins(1976). If the data exhibits no ap-parent deviation from stationarity and if it has rapidly decreasing autocorrelation function then a suitable ARIMA(p,q) model is fit to the given data. Selection of the orders of p and q is one of the crucial steps in Time Series Analysis. Most of the methods to determine p and q are based on the autocorrelation function and partial autocor-relation function as suggested by Box and Jenkins (1976). many new techniques have emerged in the literature and it is found that most of them are over very little use in determining the orders of p and q when both of them are non-zero. The Durbin-Levinson algorithm and Innovation algorithm (Brockwell and Davis 1987) are used as recur-sive methods for computing best linear predictors in an ARMA(p,q)model. These algorithms are modified to yield an effective method for ARMA model identification so that the values of order p and q can be determined from them. The new method is developed and its validity and usefulness is illustrated by many theoretical examples. This method can also be applied to an real world data.

• KIEE International Transactions on Power Engineering
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• v.4A no.4
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• pp.254-261
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• 2004

Traditionally, electrical power systems had formed the vertically integrated industry structures based on the economics of scale. However, power systems have been recently reformed to increase their energy efficiency. According to these trends, the Korean power industry underwent partial reorganization and competition in the generation market was initiated in 2001. In competitive electric markets, accurate load data is one of the most important issues to maintaining flexibility in the electric markets as well as reliability in the power systems. In practice, the measuring load data can be uncertain because of mechanical trouble, communication jamming, and other issues. To obtain reliable load data, an efficient evaluation technique to adjust the missing load data is required. This paper analyzes the load pattern of historical real data and then the tuned ARIMA (Autoregressive Integrated Moving Average), PCHIP (Piecewise Cubic Interpolation) and Branch & Bound method are applied to seek the missing parameters. The proposed method is tested under a variety of conditions and also tested against historical measured data from the Korea Energy Management Corporation (KEMCO).

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.75-87
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• 2023

We designed to employ an Artificial Intelligence learning model to predict real estate prices and determine the reasons behind their changes, with the goal of using the results as a guide for policy. Numerous studies have already been conducted in an effort to develop a real estate price prediction model. The price prediction power of conventional time series analysis techniques (such as the widely-used ARIMA and VAR models for univariate time series analysis) and the more recently-discussed LSTM techniques is compared and analyzed in this study in order to forecast real estate prices. There is currently a period of rising volatility in the real estate market as a result of both internal and external factors. Predicting the movement of real estate values during times of heightened volatility is more challenging than it is during times of persistent general trends. According to the real estate market cycle, this study focuses on the three times of extreme volatility. It was established that the LSTM, VAR, and ARIMA models have strong predictive capacity by successfully forecasting the trading price index during a period of unusually high volatility. We explores potential synergies between the hybrid artificial intelligence learning model and the conventional statistical prediction model.

• Journal of the Korean Institute of Gas
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• v.23 no.5
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• pp.52-58
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• 2019

In this study, we have developed a forecasting model for city- gas acceptance. City-gas corporations have to report about city-gas sale volume next year to KOGAS. So it is a important thing to them. Factors influenced city-gas have differences corresponding to usage classification, however, in city-gas acceptence, it is hard to classificate. So we have considered tha outside temperature as factor that influence regardless of usage classification and the model development was carried out. ARIMA, one of the traditional time series analysis, and LSTM, a deep running technique, were used to construct forecasting models, and various Ensemble techniques were used to minimize the disadvantages of these two methods.Experiments and validation were conducted using data from JB Corp. from 2008 to 2018 for 11 years.The average of the error rate of the daily forecast was 0.48% for Ensemble LSTM, the average of the error rate of the monthly forecast was 2.46% for Ensemble LSTM, And the absolute value of the error rate is 5.24% for Ensemble LSTM.

• Journal of Korea Port Economic Association
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• v.37 no.1
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• pp.179-196
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• 2021

Port congestion rate at Busan Port has increased for three years. Port congestion causes container reconditioning, which increases the dockyard labor's work intensity and ship owner's waiting time. If congestion is prolonged, it can cause a drop in port service levels. Therefore, this study proposed an anomaly detection method using ARIMA(Autoregressive Integrated Moving Average) model with the daily volume data from 2013 to 2020. Most of the research that predicts port volume is mainly focusing on long-term forecasting. Furthermore, studies suggesting methods to utilize demand forecasting in terms of port operations are hard to find. Therefore, this study proposes a way to use daily demand forecasting for port anomaly detection to solve the congestion problem at Busan port.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.255-263
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• 2024

GDP represents the total market value of goods and services produced by all economic entities, including households, businesses, and governments in a country, during a specific time period. It is a representative economic indicator that helps identify the size of a country's economy and influences government policies, so various studies are being conducted on it. This paper presents a GDP growth rate forecasting model based on a dynamic factor model using key macroeconomic indicators of G20 countries. The extracted factors are combined with various regression analysis methodologies to compare results. Additionally, traditional time series forecasting methods such as the ARIMA model and forecasting using common components are also evaluated. Considering the significant volatility of indicators following the COVID-19 pandemic, the forecast period is divided into pre-COVID and post-COVID periods. The findings reveal that the dynamic factor model, incorporating ridge regression and lasso regression, demonstrates the best performance both before and after COVID.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.1
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• pp.111-115
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• 2024

This study builds a machine learning model optimized for clocks among various techniques in the field of artificial intelligence and applies it to clock stabilization or synchronization technology based on atomic clock noise characteristics. In addition, the possibility of providing stable source clock data is confirmed through the characteristics of machine learning predicted values during holdover of atomic clocks. The proposed machine learning model is evaluated by comparing its performance with the AutoRegressive Integrated Moving Average (ARIMA) model, an existing statistical clock prediction model. From the results of the analysis, the prediction model proposed in this study (MSE: 9.47476) has a lower MSE value than the ARIMA model (MSE: 221.2622), which means that it provides more accurate predictions. The prediction accuracy is based on understanding the complex nature of data that changes over time and how well the model reflects this. The application of a machine learning prediction model can be seen as a way to overcome the limitations of the statistical-based ARIMA model in time series prediction and achieve improved prediction performance.