• Journal of Environmental Health Sciences
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• v.17 no.2
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• pp.1-8
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• 1991

The stochastic variance and structures of time series data on air quality were examined by employing the techniques of autocorrelation function, variance spectrum, fourier series, ARIMA model. Among the air quality properties of atmosphere, SO$_{2}$ is one of the most siginificant and widely measured parameters. In the study, the air quality data were included hourly observations on SO$_{2}$ TSP and O$_{3}$. The data were measured by automatic recording instrument installed in Kwanghwamoon during February and March in 1991. The results of study were as follows 1. Hourly air quality series varied with the domiant 24 hour periodicity and the 12 hour periodic variation was also observed. 2. The correlation coefficients between SO$_{2}$ and O$_{3}$ is -0.4735. 3. In simulating or forecasting variation in SO$_{2}$ ARIMA models are on a useful tools. The multiplicative seasonal ARIMA (1, 1, 0) (0, 2, 1)$_{24}$ model provided satisfactory results for hourly SO$_{2}$ time series.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.321-328
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• 2000

Working day variations caused by trading days, holidays and leap year make it difficult to analyze monthly or quarterly economic time series. In this paper, we adjust working day variations from economic time series via RegARIMA model.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.19-22
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• 1996

A traditional approach to the prediction of economic and financial variables takes the form of statistical models to summarize past observations and to project them into the envisioned future. Over the past decade, an increasing number of organizations has turned to the use of neural networks. To date, however, many spheres of interest still lack a systematic evaluation of the statistical and neural approaches. One of these lies in the prediction of corporate bond yields for Korea. This paper reports on a comparative evaluation of ARIMA models and neural networks in the context of interest rate prediction. An additional experiment relates to an integration of the two methods. More specifically, the statistical model serves as a filter by providing estimtes which are then used as input into the neural network models.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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• pp.35-41
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• 2014

Real-life time series characteristic data has significant amount of non-stationary components, especially periodic components in nature. Extracting such components has required many ad-hoc techniques with external parameters set by users in a case-by-case manner. In this study, we used Empirical Mode Decomposition Method from Hilbert-Huang Transform to extract them in a systematic manner with least number of ad-hoc parameters set by users. After the periodic components are removed, the remaining time-series data can be analyzed with traditional methods such as ARIMA model. Then we suggest a different way of setting control chart limits for characteristic data with periodic components in addition to ARIMA components.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1037-1046
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• 2008

Recently, the oil suppliers who supply the tax-exempt oil to the fishery are confronted with big trouble in their supply and demand system due to the unstable global oil prices. We applied the seasonal ARIMA(SARIMA) model to the low-sulfur and high-sulfur crude oil which are in great request and developed forecasting systems for them. Since there are many parameters in SARIMA, it is difficult to estimate the optimal parameters, but it is overcome by using simulation looping program. In conclusion, we found that the obvious seasonality in demand of low-sulfur and these demands are tending downwards gradually.

• Journal of applied mathematics & informatics
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• v.39 no.1_2
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• pp.239-249
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• 2021

This study attempts to predict the price of gold futures, a real financial product, using ARIMA and LSTM. The wavelet analysis was applied to the data to predict the price of gold futures through LSTM and ARIMA. As results, it is confirmed that the prediction performance of the existing model of predict was improved. the case of predict of price of gold futures, we confirmed that the use of a deep learning model that is not affected by the non-stationary series data is suitable and the possibility of improving the accuracy of prediction through wavelet analysis.

• Journal of Applied Reliability
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• v.18 no.1
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• pp.20-32
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• 2018

Purpose: The purpose of this study is to set up an anomaly detection criteria for sensor data coming from a motorcycle. Methods: Five sensor values for accelerator pedal, engine rpm, transmission rpm, gear and speed are obtained every 0.02 second from a motorcycle. Exploratory data analysis is used to find any pattern in the data. Traditional process control methods such as X control chart and time series models are fitted to find any anomaly behavior in the data. Finally unsupervised learning algorithm such as k-means clustering is used to find any anomaly spot in the sensor data. Results: According to exploratory data analysis, the distribution of accelerator pedal sensor values is very much skewed to the left. The motorcycle seemed to have been driven in a city at speed less than 45 kilometers per hour. Traditional process control charts such as X control chart fail due to severe autocorrelation in each sensor data. However, ARIMA model found three abnormal points where they are beyond 2 sigma limits in the control chart. We applied a copula based Markov chain to perform statistical process control for correlated observations. Copula based Markov model found anomaly behavior in the similar places as ARIMA model. In an unsupervised learning algorithm, large sensor values get subdivided into two, three, and four disjoint regions. So extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior in the sensor values. Conclusion: Exploratory data analysis is useful to find any pattern in the sensor data. Process control chart using ARIMA and Joe's copula based Markov model also give warnings near similar places in the data. Unsupervised learning algorithm shows us that the extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior.

• Journal of Agricultural Extension & Community Development
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• v.20 no.2
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• pp.511-533
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• 2013

The study used a seasonal ARIMA model to forecast the number of tourists to Kyeongju foreign in a uni-variable time series. Time series monthly data for the investigation were collected ranging from 1995 to 2010. A total of 192 observations were used for data analysis. The date showed that a big difference existed between on-season and off-season of the number of foreign tourists in Kyeongju. In the forecast multiplicative seasonal ARIMA(1,1,0) $(4,0,0)_{12}$ model was found the most appropriate model. Results show that the number of tourists was 694 thousands in 2011, 715 thousands in 2012, 725 thousands in 2013, 738 thousands in 2014, and 884 thousands in 2015. It was suggested that the grasping of the Kyeongju forecast model was very important in respect of how experts in tourism development, policy makers or planners would establish marketing strategies to allocate services in Kyeongju as a tourist destination and provide tourism facilities efficiently.

• The Journal of the Korea Contents Association
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• v.15 no.6
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• pp.399-410
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• 2015

The primary goal of the study is to examine the possibility of applying the time series model to forecasting demand and supply of social services. In the study, we used survey data based on a nationally represented sample which is secondary processed data. We selected developmental rehabilitation service. The analysis, we made models of a demand and a supply using time series analysis. Utilizing the estimates, we identified each model's pattern. This study provides an empirical evidence to suggest benefits of using the time series model for forecasting the demand and the supply pattern of newly introduced social services. We also provide discussions on policy implications of utilizing demand and supply time series models in the process of developing new social services.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1395-1399
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• 2010

최근 들어 전 세계적으로 태풍과 가뭄 그리고 국지적인 호우 등의 기상변화로 인하여 수자원 종합적인 개발과 이용계획에 대한 전문적인 예측이 필요하다. 우리나라는 홍수기에 집중적인 강우 발생으로 인하여 평수기와 유입량 차이가 심한 수문특성을 가지고 있어 안정적인 수자원 공급에 대한 장기적인 관점에서 이수와 치수정책을 수립해야 한다. 본 연구는 1985년 1월부터 2008년 12월까지 24년에 해당하는 한정된 기간의 짧은 유출량 자료를 갖는 대청댐 유역에서의 시계열 유입량 특성을 Box-Jenkins모형 또는 ARIMA모형을 적용하여 추계학적 분석을 실시하였다. 월유입량과 같은 비정상성 시계열에 적용될 수 있는 적절한 추계학적 모형을 찾기 위하여 모형의 식별과 모형의 추정, 모형의 검진 등의 3단계에 걸친 분석을 실시하였다. 연구결과 대청댐 월유입량 예측모형으로 승법계절 ARIMA$(0,1,2){\times}(1,1,0)_{12}$이 유도되었으며, 이 모형으로 1, 3, 6, 12개월의 선행기간에 대한 실시간 유입량을 예측하였다. 예측된 유입량을 2008년 실측유입량과 비교한 결과 6개월에 대한 예측의 정확성이 가장 높게 나타났다. 또한 평수기와 홍수기를 구분한 예측도 실시하였으며, 평수기는 1개월 홍수기는 3개월 간격으로 예측하는 것이 가장 적절한 것으로 분석되었다.