• 유통과학연구
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• 제21권7호
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• pp.65-71
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• 2023

Purpose: This study attempted to analyze the impacts of the backward linkage and output multipliers, and investigate the price fluctuation and the price forecast amongst the manufacturing sectors associated with food processing industrial output of Thailand. Research design, data and methodology: The Thailand Input-Output table with a size of 180 x 180 sectors from 2005, 2010, and 2015 was utilized while the secondary data of the time series from January 2002 to December 2021 were processed via a multiplicative model and Box-Jenkins model. Results: The backward linkage analysis indicates that canning and preserving of the meat sector majorly utilized the factors of production from the slaughtering sector; canning and preservation of fish and other seafoods sector largely used those factors from the ocean and coastal fishing sector; and the sugar sector used those of the sugarcane sector. Notably, the output multiplier analysis indicated that output multipliers of those 3 manufacturing sectors were highly increased; meanwhile the price fluctuation continually existed in all forms. Besides, the price forecast suggested that prices of chicken and sugarcane tended to be higher; whereas, the price of shrimp was unstable. Conclusions: Food processing industry contains the favorable components to be one of the industries of the future of Thailand.

• 농촌지도와개발
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• 제20권2호
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• pp.511-533
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• 2013

이 연구는 단변량 시계열 중에서 계절 아리마 모형을 이용하여 경주지역 외국인 관광객을 예측하고자 한다. 이 연구를 위한 시계열 월별 자료는 1995년부터 2010년까지 수집하였다. 총 192개의 관측치를 분석에 사용하였는데, 성수기와 비수기의 관광객 차이가 아주 큰 것을 보여주고 있다. 예측분석결과, 경주지역의 외국인 관광객에 대한 최종 예측모형은 승법계절 ARIMA(1,1,0) $(4,0,0)_{12}$ 모형으로 선정되었다. 이 모형에 적용하여 미래의 경주지역의 외국인 관광객은 2011년 694천명, 2012년 715천명, 2013년 725천명, 2014년 738천명, 2015년 751천명이 방문하는 것으로 나타났다. 이는 향후 경주지역 외국인 관광객에게 효율적으로 관광시설을 공급함과 동시에 서비스를 제공하기 위한 관광정책을 수립하는 측면에서 관광관련 이해당사자들에게 매우 중요하다는 것을 시사하고 있다.

• 한국가스학회지
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• 제23권5호
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• pp.52-58
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• 2019

본 연구에서는 국내 도시가스 인수량에 대한 예측 모델을 개발하였다. 국내의 도시가스 회사는 KOGAS에 차년도 수요를 예측하여 보고해야 하므로 도시가스 인수량 예측은 도시가스 회사에 중요한 사안이다. 도시가스 사용량에 영향을 미치는 요인은 용도구분에 따라 다소 상이하나, 인수량 데이터는 용도별 구분이 어렵기 때문에 특정 용도에 관계없이 영향을 주는 요인으로 외기온도를 고려하여 모델개발을 실시하였다.실험 및 검증은 JB주식회사의 2008년부터 2018년까지 총 11년 치 도시가스 인수량 데이터를 사용하였으며, 전통적인 시계열 분석 중 하나인 ARIMA(Auto-Regressive Integrated Moving Average)와 딥러닝 기법인 LSTM(Long Short-Term Memory)을 이용하여 각각 예측 모델을 구축하고 두 방법의 단점을 최소화하기 위하여 다양한 앙상블(Ensemble) 기법을 사용하였다. 본 연구에서 제안한 일별 예측의 오차율 절댓값 평균은 Ensemble LSTM 기준 0.48%, 월별 예측의 오차율 절댓값 평균은 2.46%, 1년 예측의 오차율 절댓값 평균은 5.24%임을 확인하였다.

• 한국항만경제학회지
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• 제20권2호
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• pp.151-167
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• 2004

Traditional traffic forecast has employed regression analysis or time-series analysis based on past trends of explanatory variables. However, not existing but planned port facilities do not have historical data for traffic estimation. Consequently, arbitrary traffic allocation has been subject to researcher's intuition. In this paper, container throughput at New Incheon Outer-South Port will be estimated using stated preference(SP) and sample enumeration methodology on the basis of survey data about the choice behaviors of port users in a theoretical situation. In the SP survey, shippers, freight forwarders and carriers were required to answer a choice between two alternative ports: Busan and Incheon. Although total 27 scenarios of questionnaires were constructed with 3 levels of 3 explanatory variables, each interviewee was asked to answer for just 9 scenarios chosen at random. A binary choice logit model was applied to the survey data. The elasticity of travel time is estimated to be very high, implying that building New Incheon Outer-South Port could be effective in relieving the congestion of the Kyungin corridor. The analysis result shows that increasing service level at Incheon Port would bring in the substantial diversion of container cargo in the Capital region to Incheon Port from Busan Port.

• 디지털산업정보학회논문지
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• 제9권2호
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• pp.121-137
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• 2013

Now days, firm's technology capability is recognized as important factor to forecast and to evaluate firm's business performance. There are many efforts to develop useful indicators by applying patent information that includes concrete description about technology. Many previous studies analyzed relationship between patent indicators and firm's performance. But they didn't consider time gap between a point of firm's invention activity and a point of firm's performance improvement. They didn't considered a character of industrial fields either. To overcome these limitations, we selected IT industry for target analysis industry. Time-series patent data and financial data from 41 American IT firms between 2000 and 2011 were used to analyze. In this study, We empirically analyzed subsequent effect of patent indicators on firm's business performance by using correlation analysis and regression analysis.

• 한국측량학회지
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• 제25권2호
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• pp.133-140
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• 2007

본 연구는 도시 발전에 따른 여러 가지 도시문제들을 예측하고 대처하기 위해 도시내 토지형태가 어떻게 변화되었는지 모니터하였다. 충주시가 촬영된 Landsat 위성영상을 대상으로 도로주변의 피복상태를 분석하고, 그 분포양상을 시계열로 비교분석하여 변화량을 계산하였다. 이를 위해 원격탐사의 공간분석기술과 GIS기술을 통합하여, 시각적으로 두 시기의 영상을 판독하고 정량적 면적변화를 추출하였다. 그 결과 위성자료와 지형공간정보들의 지속적인 분석과 통계분석을 통하여 도심지역의 발전 모형을 생성 할 수 있었다.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2002년도 추계 학술발표회 논문집
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• pp.265-272
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• 2002

An Integer-valued autoregressive integrated (INARI) model is introduced to eliminate stochastic trend and seasonality from time series of count data. This INARI extends the previous integer-valued ARMA model. We show that it is stationary and ergodic to establish asymptotic normality for conditional least squares estimator. Optimal estimating equations are used to reflect categorical and serial correlations arising from panel count data and variations arising from three random processes for obtaining observation into estimation. Under regularity conditions for martingale sequence, we show asymptotic normality for estimators from the estimating equations. Using cancer mortality data provided by the U.S. National Center for Health Statistics (NCHS), we apply our results to estimate the probability of cells classified by 4 causes of death and 6 age groups and to forecast death count of each cell. We also investigate impact of three random processes on estimation.

• East Asian mathematical journal
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• 제35권3호
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• pp.305-318
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• 2019

Over the past decades, the Lee-Carter model [1] has attracted much attention from various demography-related fields in order to project the future mortality rates. In the Lee-Carter model, the speed of mortality improvement is stochastically modeled by the so-called mortality index and is used to forecast the future mortality rates based on the time series analysis. However, the modeling is applied to long time series and thus an important structural change might exist, leading to potentially large long-term forecasting errors. Therefore, in this paper, we are interested in detecting the structural change of the Lee-Carter model and investigating the actuarial implications. For the purpose, we employ the tests proposed by Coelho and Nunes [2] and analyze the mortality data for six countries including Korea since 1970. Also, we calculate life expectancies and whole life insurance premiums by taking into account the structural change found in the Korean male mortality rates. Our empirical result shows that more caution needs to be paid to the Lee-Carter modeling and its actuarial applications.

• Journal of Information Processing Systems
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• 제15권5호
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• pp.1201-1210
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• 2019

Stock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known to have a direct influence on the stock markets globally. Given that the stock price data often contain both linear and non-linear patterns, no single model can be adequate in modelling and predicting time series data. The autoregressive integrated moving average (ARIMA) model cannot deal with non-linear relationships, however, it provides an accurate and effective way to process autocorrelation and non-stationary data in time series forecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. As a result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing price of the Shanghai composite index and Shenzhen component index.

• Communications for Statistical Applications and Methods
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• 제7권1호
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• pp.97-106
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• 2000

In time series analysis we generally use Box-Cox power transformation for variance stabilization. In this paper we show that order estimator and one step ahead forecast of transformed AR(1) model are approximately invariant to those of the original model under some assumptions. A small Monte-Carlo simulation is performed to support the results.