• Journal of Korean Society of Transportation
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• v.25 no.1 s.94
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• pp.121-132
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• 2007

Up to now Permanent traffic volumes have been counted by Automatic Vehicle Classification (AVC) on National Highways. When counted data have missing items or errors, the data must be revised to stay statistically reliable This study was carried out to estimate correct data based on outoregression and seasonal AutoRegressive Integrated Moving Average (ARIMA). As a result of verification through seasonal ARIMA, the longer the missed period is, the greater the error. Autoregression results in better verification results than seasonal ARIMA. Traffic data is affected by the present state mote than past patterns. However. autoregression can be applied only to the cases where data include similar neighborhood patterns and even in this case. the data cannot be corrected when data are missing due to low qualify or errors Therefore, these data shoo)d be corrected using past patterns and seasonal ARIMA when the missing data occurs in short periods.

• Industrial Engineering and Management Systems
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• v.8 no.4
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• pp.257-263
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• 2009

Focusing on the idea that the equation of exponential smoothing method (ESM) is equivalent to (1, 1) order ARMA model equation, new method of estimation of smoothing constant in exponential smoothing method is proposed before by us which satisfies minimum variance of forecasting error. Theoretical solution was derived in a simple way. Mere application of ESM does not make good forecasting accuracy for the time series which has non-linear trend and/or trend by month. A new method to cope with this issue is required. In this paper, combining the trend removal method with this method, we aim to improve forecasting accuracy. An approach to this method is executed in the following method. Trend removal by a linear function is applied to the original shipping data of consumer goods. The combination of linear and non-linear function is also introduced in trend removal. For the comparison, monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful especially for the time series that has stable characteristics and has rather strong seasonal trend and also the case that has non-linear trend. The effectiveness of this method should be examined in various cases.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.475-485
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• 2014

A stochastic weather generator based on a generalized linear model (GLM) approach is a commonly used tools to simulate a time series of daily weather. In this paper, we propose a multi-site weather generator with applications to historical data in South Korea. The proposed method extends the approach of Kim et al. (2012) by considering spatial dependence in the model. To reduce this phenomenon, we also incorporate a time series of seasonal mean precipitations of South Korea in the GLM weather generator as a covariate. Spatial dependence was incorporated into the model through a latent Gaussian process. We apply the proposed model to precipitation data provided by 62 stations in Korea from 1973{2011.

• The Korean Journal of Applied Statistics
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• v.27 no.5
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• pp.843-853
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• 2014

Accurate electricity demand forecasting for daily peak load is essential for management and planning at electrical facilities. In this paper, we rst, introduce the several time series models that forecast daily peak load and compare the forecasting performance of the models based on Mean Absolute Percentage Error(MAPE). The results show that the Reg-AR-GARCH model outperforms other competing models that consider Cooling Degree Day(CDD) and Heating Degree Day(HDD) as well as seasonal components.

• Environmental Engineering Research
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• v.22 no.2
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• pp.162-168
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• 2017

Rainfall is one of the most important phenomena of the natural system. In Bangladesh, agriculture largely depends on the intensity and variability of rainfall. Therefore, an early indication of possible rainfall can help to solve several problems related to agriculture, climate change and natural hazards like flood and drought. Rainfall forecasting could play a significant role in the planning and management of water resource systems also. In this study, univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used to forecast monthly rainfall for twelve months lead-time for thirty rainfall stations of Bangladesh. The best SARIMA model was chosen based on the RMSE and normalized BIC criteria. A validation check for each station was performed on residual series. Residuals were found white noise at almost all stations. Besides, lack of fit test and normalized BIC confirms all the models were fitted satisfactorily. The predicted results from the selected models were compared with the observed data to determine prediction precision. We found that selected models predicted monthly rainfall with a reasonable accuracy. Therefore, year-long rainfall can be forecasted using these models.

• Journal of Environmental Science International
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• v.16 no.3
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• pp.369-376
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• 2007

This study was conducted to evaluate water quality in coastal sea of Kunsan using multivariate analysis. The analysis data in Coastal Sea of Kunsan use of surveyed data by the NFRDI from April 2000 to November 2002. Twelve water Quality parameter were determined on each sample. The results was summarized as follow ; Water quality in coastal sea of Kunsan could be explained up to 62.782% by four factors which were included in loading of nitrogen-nutrients by Keum river(24.688%), suspended solids variation (12.180%), seasonal climate variation (18.367%) and variation of DIP (10.546%). To analyze spatially and monthly variation by factor score, it was divided by inner area and outer area spatially, and spring and summer monthly. The result of time series analysis by factor score, inner area of Kunsan coastal sea(St.1 and St. 2) was the most affected by nitrogen-nutrient and suspended solids due to runoff by Keum river. It could be suggested from these results that it is important to reduce tile pollution loads from Kuem river for the control of the water quality in coastal sea of Kunsan.

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.237-252
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• 2013

An estimation model for premiums and components is essential to determine reasonable insurance premiums. In this study, we introduce diverse models for the estimation of property damage premiums(premium, depth and frequency) that include a regression model using a dummy variable, additive independent variable model, autoregressive error model, seasonal ARIMA model and intervention model. In addition, the actual property damage premium data was used to estimate the premium, depth and frequency for each model. The estimation results of the models are comparatively examined by comparing the RMSE(Root Mean Squared Errors) of estimates and actual data. Based on real data analysis, we found that the autoregressive error model showed the best performance.

• Asian Journal of Business Environment
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• v.14 no.3
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• pp.1-11
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• 2024

Purpose: This study analyzes the impact of weather conditions, holidays, and sporting events on beer sales, providing insights for market strategy and inventory management in the beer industry. Research design, data and methodology: Beer types were classified into Lagers and Ales, with further subcategories. The study utilized weekly retail sales data from January 2018 to August 2020, provided by Nielsen Korea. An ARMAX model was employed for time-series analysis. Results: The analysis revealed that increasing temperatures positively influence sales of Pilsners and Pale Lagers. Conversely, higher precipitation levels negatively affect overall Lager sales. Among Ales, only Stout sales showed a significant decrease with increased rainfall. Sunshine duration did not significantly impact sales for any beer type. Humidity generally had little effect on beer sales, with the exception of Amber Lagers, which showed sensitivity to humidity changes. Holidays and sporting events were found to significantly boost sales across most beer types, although the specific impacts varied by beer category. Conclusions: This study offers a detailed analysis of how weather conditions and specific events influence different beer type sales. The findings provide valuable insights for breweries, beer processors, and retailers to optimize their market strategies and inventory management based on weather forecasts and seasonal events. By understanding the consumption patterns of each beer type in relation to environmental factors, businesses can better anticipate demand fluctuations and tailor their operations accordingly.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.4
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• pp.331-338
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• 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 Navigation and Port Research
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• v.32 no.6
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• pp.425-431
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• 2008

The future cargo transportation and traffic volume on container in Gwangyang port was forecasted by using univariate time series models in this research. And the container ship traffic was produced. The constructed models all were most adapted to Winters' additive models with a trend and seasonal change. The cargo transportation on container in Gwangyang port was estimated each about 2,756 thousand TEU and 4,470 thousand TEU in 2011 and 2015 by increasing each 7.4%, 16.2% compared with 2007. The volume per ship on container was estimated each about 675TEU and 801TEU in 2011 and 2015 by increasing each 30.3%, 54.6% compared with 2007. Also, traffic volume on container incoming in Gwangyang Port was prospected each about 4,078ships and 5,921ships in 2011 and 2015.