• Communications for Statistical Applications and Methods
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• v.15 no.5
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• pp.719-726
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• 2008

In this paper we consider a joint test for seasonal cointegrating(CI) ranks that enables us to simultaneously model cointegrated structures across seasonal unit roots in seasonal cointegration. A CI rank test for a single seasonal unit root is constructed and extended to a joint test for multiple seasonal unit roots. Their asymptotic distributions and selected critical values for the joint test are obtained. Through a small Monte Carlo simulation study, we evaluate performances of the tests.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.563-573
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• 2018

An Expert Seasonal Prediction System for operational Seasonal Outlook (ESPreSSO) is developed based on the APEC Climate Center (APCC) Multi-Model Ensemble (MME) dynamical prediction and expert-guided statistical downscaling techniques. Dynamical models have improved to provide meaningful seasonal prediction, and their prediction skills are further improved by various ensemble and downscaling techniques. However, experienced scientists and forecasters make subjective correction for the operational seasonal outlook due to limited prediction skills and biases of dynamical models. Here, a hybrid seasonal prediction system that grafts experts' knowledge and understanding onto dynamical MME prediction is developed to guide operational seasonal outlook in Korea. The basis dynamical prediction is based on the APCC MME, which are statistically mapped onto the station-based observations by experienced experts. Their subjective selection undergoes objective screening and quality control to generate final seasonal outlook products after physical ensemble averaging. The prediction system is constructed based on 23-year training period of 1983-2005, and its performance and stability are assessed for the independent 11-year prediction period of 2006-2016. The results show that the ESPreSSO has reliable and stable prediction skill suitable for operational use.

• Communications for Statistical Applications and Methods
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• v.18 no.2
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• pp.237-244
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• 2011

In call forecasting literature, both the seasonal autoregressive integrated moving average(ARIMA) type models and seasonal linear models have been popularly suggested as competing models. However, their parallel comparison for the forecasting accuracy was not strictly investigated before. This study evaluates the accuracy of both the seasonal linear models and the seasonal ARIMA-type models when predicting intra-day call arrival rates using both real and simulated data. The seasonal linear models outperform the seasonal ARIMA-type models in both one-day-ahead and one-week-ahead call forecasting in our empirical study.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.785-799
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• 2018

With the development of renewable energy sector, the importance of solar energy is continuously increasing. Solar radiation forecasting is essential to accurately solar power generation forecasting. In this paper, we used time series models (ARIMA, ARIMAX, seasonal ARIMA, seasonal ARIMAX, ARIMA GARCH, ARIMAX-GARCH, seasonal ARIMA-GARCH, seasonal ARIMAX-GARCH). We compared the performance of the models using mean absolute error and root mean square error. According to the performance of the models without exogenous variables, the Seasonal ARIMA-GARCH model showed better performance model considering the problem of heteroscedasticity. However, when the exogenous variables were considered, the ARIMAX model showed the best forecasting accuracy.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.627-633
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• 2020

The study attempts to analyze the impact of firm's risk on capital structure in the context of seasonal and non-seasonal businesses. We use two independent variables namely credit risk and systematic risk and one dependent variable to explore this connection. Sugar sector is taken as seasonal while the textile sector as non-seasonal businesses. The panel data of twenty-five firms from each sector are taken ranging for the period of 2012 to 2019 which has been retrieved from their annual reports for empirical analysis of the study. The results reveal the negative impact of credit risk on capital structure in both types of businesses. Increasing (decreasing) one point of credit risk causes a decrease (increase) leverage ratio by 0.27 points for seasonal while increasing (decreasing) one point of credit risk causes to decrease (increase) leverage by 0.15 points for non-seasonal businesses. Furthermore, the study shows positive impact of systematic risk on leverage ratio in non-seasonal business and no impact in seasonal business. Any increase (decrease) in the systematic risk causes an incline (decline) leverage ratio by 2.68 units for non-seasonal businesses. The study provides a guideline to managers for risk management in businesses. The research focusses on theoretical as well as managerial and policy implications on risk management in businesses.

• Journal of Korean Society of Rural Planning
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• v.21 no.3
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• pp.19-31
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• 2015

In this study, seasonal customs are analyze the appropriate festival for each 24 seasonal divisions of the year. Recover the intrinsic value of the festival and selected the target villages and each village should investigate elements of the festival. The village were sought activation of a rural village by setting the festive season. (1) 24 seasonal divisions of the year festival is a small town festival, the festival's program is based on the experience-oriented. Based on period seasonal customs and season plays are set up 24 seasonal divisions of the year festival's program. (2) Survey area is the rural tourism village carried seven villages at Cheongju-si in Chungcheongbuk-do. (3) 24 seasonal divisions of the year festival establish as possible to the festival program is based on 24 seasonal divisions of the yearfestival seasonal customs, seven villages were set on the festival. on the season sesipung through the festive season as possible to the festival program was set up, seven villages were set on the festival. The first standard, festivals and events that are currently made. Second,now ongoing in the village is experience program and a 24 seasonal divisions of the year seasonal customs resource. (4) As a result, each period of the festival was set in the village. (5) By festival setting recover intrinsic value of the festival by taking advantage of 24 seasonal divisions of the year. The common interests of the rural town of experience, the rising interest in each town and village festivals activation of the network can be achieved.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.217-225
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• 2011

In this paper, a seasonal variable selection method using the spectral analysis accompanied with seasonal linear model is suggested. The suggested method is applied to the prediction of intra-day call arrivals at a large North American commercial bank call center and a signi cant intra-month seasonal variable I detected. This newly detected seasonal factor is included in the seasonal linear model and is compared with the seasonal linear models without this variable to see whether the new variable helps to improve the forecasting performance. The seasonal linear model with the new variable outperformed the models without it in one-day-ahead forecasting.

• Journal of the Korean Statistical Society
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• v.36 no.1
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• pp.149-156
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• 2007

This study suggests a new method for testing seasonal unit roots in a momentum threshold autoregressive (MTAR) process. This sign test is robust against heteroscedastic or heavy tailed errors and is invariant to monotone data transformation. The proposed test is a seasonal extension of the sign test of Park and Shin (2006). In the case of partial seasonal unit root in an MTAR model, a Monte-Carlo study shows that the proposed test has better power than the seasonal sign test developed for AR model.

• Journal of Communications and Networks
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• v.13 no.6
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• pp.621-624
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• 2011

With the rapid growth of internet traffic, accurate and reliable prediction of internet traffic has been a key issue in network management and planning. This paper proposes an autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH) error model for forecasting internet traffic and evaluates its performance by comparing it with seasonal autoregressive integrated moving average (ARIMA) models in terms of root mean square error (RMSE) criterion. The results indicated that the seasonal AR-GARCH models outperformed the seasonal ARIMA models in terms of forecasting accuracy with respect to the RMSE criterion.

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
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• v.12 no.2
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• pp.101-113
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• 1983

논문은 물을 저장물질로 사용하는 공간난방에 있어서의 seasonal storage의 몇 가지 중요한 문제연구하고 있다. 집열기 면적과 저장체적 그리고 시스템 성능간의 관계를 transient simulation program(TRNSYS)을 사용하여 조사하였다. 여기서 seasonal storage의 가장 일어나기 쉬운 상태가 나타내어지는데 seasonal storage system의 설계에 특히 역점을 두고 있다. 이러한 설계방법은 몇 일간에서 seasonal storage에 이르는 seasonal pacity (저장용량)에 대하여 적용되어진다. 비용과 관련하여 이러한 설계방법은 seasonal storage system 경제성 (economic viability)을 추정하는데 유용할 것이다. 또한 시스템 설계에서 부하 열기의 크기 탱크단열 집열기 경사 매년 기후변화의 중요성이 조사되고 있다.