• Journal of Convergence for Information Technology
• /
• v.9 no.12
• /
• pp.54-61
• /
• 2019

Recently, IITS (intelligent integrated transportation system) has been important topic in Smart City related industry. As a main objective of IITS, prevention of traffic jam (due to car accidents) has been attempted with help of advanced sensor and communication technologies. Studies show that car accident has certain correlation with some factors including characteristics of location, weather, driver's behavior, and time of day. We concentrate our study on observing auto correlativity of car accidents in terms of time of day. In this paper, we performed the ARIMA tests including ADF (augmented Dickey-Fuller) to check the three factors determining auto-regressive, stationarity, and lag order. Summary on forecasting of hourly car crash counts is presented, we show that the traffic accident data obtained in Korea can be applied to ARIMA model and present a result that traffic accidents in Korea have property of being recurrent daily basis.

• Journal of KIISE:Information Networking
• /
• v.34 no.3
• /
• pp.156-167
• /
• 2007

In this paper, we develop the model based prediction algorithm for Variable-Bit-Rate(VBR) video traffic with regular Group of Picture(GOP) pattern. We use multiplicative ARIMA process called GOP ARIMA (ARIMA for Group Of Pictures) as a base stochastic model. Kalman Filter based prediction algorithm consists of two process: GOP ARIMA modeling and prediction. In performance study, we produce three video traces (news, drama, sports) and we compare the accuracy of three different prediction schemes: Kalman Filter based prediction, linear prediction, and double exponential smoothing. The proposed prediction algorithm yields superior prediction accuracy than the other two. We also show that confidence interval analysis can effectively detect scene changes of the sample video sequence. The Kalman filter based prediction algorithm proposed in this work makes significant contributions to various aspects of network traffic engineering and resource allocation.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.25 no.4
• /
• pp.331-338
• /
• 2012

Irregularity data inspected by EM-120, an railway inspection system in Korea includes unavoidable incomplete and erratic information, so it is encountered lots of problem to analyse those data without appropriate pre-data-refining processes. In this research, for the efficient management and maintenance of railway system, characteristics and problems of the detected track irregularity data have been analyzed and efficient processing techniques were developed to solve the problems. The correlation between track irregularity and seasonal changes was conducted based on ARIMA model analysis. Finally, time series analysis was carried out by various forecasting model, such as regression, exponential smoothing and ARIMA model, to determine the appropriate optimal models for forecasting track irregularity progress.

• Journal of the Korean Institute of Gas
• /
• v.19 no.2
• /
• pp.20-28
• /
• 2015

A suitable monitoring method is necessary for successful policy implementation and its evaluation, required for effective prevention of abnormal fire occurrences. To do this, there were studies for applying control charts of quality management to fire occurrence monitoring. As a result, it was proved that more fire occurs in winter and its trend moves yearly-basis with some patterns. Although it has trend, if we apply the same criteria for each time, inefficient overreacting fire prevention policy will be accomplished in winter, and deficient policy will be accomplished in summer. Thus, applying different control limits adaptively for each time would enable better forecasting and monitoring of fire occurrences. In this study, we treat fire occurrences as time series model and propose a method for configuring its coefficients with ARIMA model. Based on this, we expect to carry out advanced analysis of fire occurrences and reasonable implementation of prevention activities.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.25 no.8A
• /
• pp.1153-1159
• /
• 2000

This paper applies a seasonal ARIMA model to the timely forecasting in a line utilization and its confidence interval on the base of the past data of the lido utilization that QoS of the network is greatly influenced by and proposes the prediction algorithm of threshold violation in line utilization using the seasonal ARIMA model. We can predict the time of threshold violation in line utilization and provide the confidence based on probability. Also, we have evaluated the validity of the proposed model and estimated the value of a proper threshold and a detection probability, it thus appears that we have maximized the performance of this algorithm.

• Korean Journal of Construction Engineering and Management
• /
• v.6 no.6 s.28
• /
• pp.98-106
• /
• 2005

The construction cost index and the forecasting model of apartment house can be efficient for evaluating the validness of the fluctuating price, and for making guidelines for construction firms when calculating their profit. In this study the previous construction cost index of apartment house was improved, and the forecasting model based on X-12 ARIMA was developed. According to the result, during the last five years the construction cost, excluding labor expense, has risen approximately to 22.7%. And during next three years, additional 16.8% rise of construction cost is expected. Those quantitative results can be utilized for evaluating the apartment house's selling price in an indirection, and be helpful to understand the variation pattern of the price.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.65 no.2
• /
• pp.1-11
• /
• 2023

This study analyzed the impact of greenhouse cultivation area and groundwater level changes due to the water curtain cultivation in the greenhouse complexes. The groundwater observation data in the Miryang study area were used and classified into greenhouse and field cultivation areas to compare the groundwater impact of water curtain cultivation in the greenhouse complex. We identified the characteristics of the groundwater time series data by the terrain of the study area and selected the optimal model through time series analysis. We analyzed the time series data for each terrain's two representative groundwater observation wells. The Seasonal ARIMA model was chosen as the optimal model for riverside well, and for plain and mountain well, the ARIMA model and Seasonal ARIMA model were selected as the optimal model. A suitable prediction model is not limited to one model due to a change in a groundwater level fluctuation pattern caused by a surrounding environment change but may change over time. Therefore, it is necessary to periodically check and revise the optimal model rather than continuously applying one selected ARIMA model. Groundwater forecasting results through time series analysis can be used for sustainable groundwater resource management.

• Proceedings of the KIPE Conference
• /
• 2019.11a
• /
• pp.210-211
• /
• 2019

배터리의 안정적인 운영과 관리를 위해서 배터리의 SOH 예측은 매우 중요한 과제이다. 본 논문에서는 배터리 팩의 SOH를 예측하기 위한 ARIMA-Kalman 기반의 최적화된 하이브리드 방법을 소개한다.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.33 no.5B
• /
• pp.304-309
• /
• 2008

This paper propose the traffic anomaly detection scheme based time series model. We apply ARIMA prediction model to this scheme and transform the value of the abnormal symptom into the probability value to maximize the traffic anomaly symptom detection. For this, we have evaluated the abnormal detection performance for the proposed model using total traffic and web traffic included the attack traffic. We will expect to have an great effect if this scheme is included in some network based intrusion detection system.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.3
• /
• pp.763-768
• /
• 2023

In this paper, the existing statistical method (ARIMA) and machine learning method (Informer) were developed and compared to predict the distribution volume of pharmaceuticals. It was found that a machine learning-based model is advantageous for daily data prediction, and it is effective to use ARIMA for monthly prediction and switch to Informer as the data increases. The prediction error rate (RMSE) was reduced by 26.6% compared to the previous method, and the prediction accuracy was improved by 13%, resulting in a result of 86.2%. Through this thesis, we find that there is an advantage of obtaining the best results by ensembleing statistical methods and machine learning methods. In addition, machine learning-based AI models can derive the best results through deep learning operations even in irregular situations, and after commercialization, performance is expected to improve as the amount of data increases.