• The Journal of Asian Finance, Economics and Business
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• 제9권10호
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• pp.1-13
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• 2022

This paper empirically explores the predicting ability of the newly proposed smooth transition (ST) time-varying combining forecast methods. The proposed method allows the "weight" of combining forecasts to change gradually over time through its unique feature of transition variables. Stock market returns from 7 countries were applied to Ad Hoc models, the well-known Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family models, and the Smooth Transition Exponential Smoothing (STES) models. Of the individual models, GJRGARCH and STES-E&AE emerged as the best models and thereby were chosen for constructing the combined forecast models where a total of nine ST combining methods were developed. The robustness of the ST combining forecasts is also validated by the Diebold-Mariano (DM) test. The post-sample forecasting performance shows that ST combining forecast methods outperformed all the individual models and fixed weight combining models. This study contributes in two ways: 1) the ST combining methods statistically outperformed all the individual forecast methods and the existing traditional combining methods using simple averaging and Bates & Granger method. 2) trading volume as a transition variable in ST methods was superior to other individual models as well as the ST models with single sign or size of past shocks as transition variables.

• KDI Journal of Economic Policy
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• 제14권1호
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• pp.121-145
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• 1992

본고(本稿)에서는 현재의 경제상황을 잘 반영하는 건설투자활동(建設投資活動)의 단기예측모형(短期豫測模型)을 정립하고자 먼저 관련 시계열자료의 안정성(安定性) 여부(與否)와 순환성(循環性), 계절성(季節性)의 특성을 살펴본 후 여러 단기모형의 예측력(豫測力), 정합성(整合性), 설명력(說明力)을 비교 검토했다. 단위근(單位根) 검정(檢定)과 자기상관계수(自己相關係數) 스펙트랄 밀도함수 분석의 결과, 건설관련 시계열자료들이 대체로 단위근(單位根)을 갖지 않음으로써 안정적이고 주기적인 순환변동을 하고 있으며, 시차변수의 설명력이 높은 특성을 나타내었다. 또한 건설투자자료의 특성이 선행지표(先行指標)인 건축허가연면적(建築許可延面積) 및 건설수주액(建設受注額)과 아주 유사하여 건설투자 단기예측에 있어서 두 지표 사이의 시차관계(時差關係) 파악이 중요함을 알 수 있었다. 제(第)III장(章)에서는 단변량(單變量) 시계열모형(時系列模型)으로 ARIMA모형(模型)과 승법선형추세예측모형(乘法線型趨勢豫測模型)을, 다변량(多變量) 시계열모형(時系列模型)으로는 첫째, 선행지표(先行指標)를 이용한 1차자기회귀모형(次自己回歸模型), VAR모형(模型), 둘째 GNP자료를 이용한 거시경제모형의 단순한 축약형모형(縮約型模型)과 VAR모형(模型)을 제시하고 이들을 비교 평가하였다. 이에 따르면 단변량 시계열모형보다는 다변량 시계열모형이 시간이 경과할수록 예측오차(豫測誤差)가 커지지 않는다는 점에서 우수한 것으로 나타났으며, 다변량모형 중에서도 벡터자기회귀모형이 여타 모형보다 절대예측오차평균(絶對豫測誤差平均), 평균자승근(平均自乘根) 퍼센트 오차(誤差), 결정계수(決定係數) 등 모든 면에서 우수한 것으로 평가되었다. 이는 최근 건설투자가 추세에서 벗어난 급증세를 지속하고 있음을 고려할 때 타당한 결론이라 생각된다.

• 동아시아식생활학회지
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• 제17권4호
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• pp.571-579
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• 2007

The aim of this study was to predict the development process of the Korean food service industry by forecasting the per capita GDP. Forecasting the GDP, involved two primary approaches. One was related to looking at the Korean food service industry's situation by per capita GDP and comparing it to that of the US and Japan. The other was to predict food service industry projections in Korea by quantitative forecasting models. Holt's simple exponential smoothing method and new types of the series models(Damped trend exponential smoothing method), were employed to predict the per capita GDP. The accuracy of the models was measured by MAPE. The empirical results of the forecasting models indicate that the three time series models performed fairly well. Of these Damped trend Damped trend exponential smoothing performed best with the lowest MAPE(9.9%). The results show that the time for reaching a per capita GDP level of $20,000 was 2008 with the Damped trend model and 2009 with the Holt model. Moreover, we found that a per capita GDP level of $30,000 will be achieved in 2012 from the Damped trend model and in 2013 from the Holt model. Within this study, the implications for the Korean food service industry are further discussed. It was predicted there will be a stabilization period in 2008 or 2009 in Korea with achievement of a per capita GDP of $20,000. At this time, major food service industry companies will need to invest in equipment toy external growth and there will be industry trends toward ethnic food and theme restaurants. Also, if a per capita GDP of $30,000 is achieved by 2012 or 2013, the Korean food industry will need to be highly responsive. Therefore, food industry companies should forecast and study customer values and prepare for changes.

• 응용통계연구
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• 제23권2호
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• pp.249-261
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• 2010

Long-term forecasting of seasonal time series is critical in many applications such as planning business strategies and resolving possible problems of a business company. Unlike the traditional approach that depends solely on dynamic models, Li and Hinich (2002) introduced a combination of stochastic dynamic modeling with filter bank approach for forecasting seasonal patterns using highly coherent(High-C) waveforms. We modify the filter selection and forecasting procedure on wavelet domain to be more feasible and compare the resulting predictor with one that obtained from the wavelet variance estimation method. An improvement over other seasonal pattern extraction and forecasting methods based on such as wavelet scalogram, Holt-Winters, and seasonal autoregressive integrated moving average(SARIMA) is shown in terms of the prediction error. The performance of the proposed method is illustrated by a simulation study and an application to the real stock price data.

• 상하수도학회지
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• 제18권6호
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• pp.758-769
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• 2004

The trends and seasonalities of most time series have a large variability. The result of the Singular Spectrum Analysis(SSA) processing is a decomposition of the time series into several components, which can often be identified as trends, seasonalities and other oscillatory series, or noise components. Generally, forecasting by the SSA method should be applied to time series governed (may be approximately) by linear recurrent formulae(LRF). This study examined forecasting ability of SSA-LRF model. These methods are applied to daily water demand data. These models indicate that most cases have good ability of forecasting to some extent by considering statistical and visual assessment, in particular forecasting validity shows good results during 15 days.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2007년도 학술발표회 논문집
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• pp.1387-1391
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• 2007

In this study, water quality prediction that is necessary to water quality forecasting system is performed using 2-D river analysis models RMA-2 and RAM4. RAM4 is suitable to water quality forecasting system cause it is possible to put in the pollutants as a mass type boundary condition. Instant injections of pollutants at Yongdamdaegyo Bridge in Namhangang River are simulated and the behavior of pollutant cloud is observed. The effects of water quality accident to Paldang 2 water intake plants in Paldangho Lake is analyzed with time variation. And extra flow simulation is performed for mitigation of pollution. Several cases of water quality forecasting system at home and abroad are investigated and the direction of water quality forecasting system is presented.

• 한국정보시스템학회:학술대회논문집
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• 한국정보시스템학회 2000년도 추계학술대회
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• pp.36-44
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• 2000

The purpose of this study is to introduce a more efficient forecasting technique, which could help result the reduction of cost in removing the waste of airline in-flight meals. We will use a neural network approach known to many researchers as the “Outstanding Forecasting Technique”. We employed a multi-layer perceptron neural network using a backpropagation algorithm. We also suggested using other related information to improve the forecasting performances of neural networks. We divided the data into three sets, which are training data set, cross validation data set, and test data set. Time lag variables are still employed in our model according to the general view of time series forecasting. We measured the accuracy of our model by “Mean Square Error”(MSE). The suggested model proved most excellent in serving economy class in-flight meals. Forecasting the exact amount of meals needed for each airline could reduce the waste of meals and therefore, lead to the reduction of cost. Better yet, it could enhance the cost competition of each airline, keep the schedules on time, and lead to better service.

• 대한산업공학회지
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• 제40권4호
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• pp.415-423
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• 2014

In recent decades, RFID (Radio Frequency IDentification) technology has been recognized as one of the most core competencies in implementing ubiquitous society. However, Korea has not seen good success in diffusion of RFID even though Korean government continues funding many projects to diffuse the technology in industries. Most previous researches overestimate the growth of Korean RFID market in contrary to real market situation. This study aims to analyze the Korean RFID market and find a reasonable forecasting model for it. Our experimental results show that Bass forecasting model provides the more realistic estimates than any other models and the analyses of forecasting error provide useful information for the better forecasting. We also observed that government policy plays a crucial role in the diffusion of RFID technology in Korea.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2009년도 추계학술대회
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• pp.720-722
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• 2009

경제 성장에 따른 국내 산업분야의 발달 및 국민 생활수준의 향상으로 전력 소비가 지속적으로 증가하고 있다. 전력을 안정적으로 공급하기 위해서는 전력 수요에 대한 중 단기 예측이 중요하며, 정확한 예측에 따라 안정적인 수급 계획을 확립할 수 있다. 본 논문에서는 부산시에서 공급되는 부산지역의 전력 데이터와 기후 관련 자료를 1995년 1월부터 2007년 12월까지의 측정치를 가지고 시계열 데이터를 수집하여 분석하고 신경회로망의 구조를 설계하여 실험을 통하여 실제 데이터와 예측 데이터를 비교 분석하고 평가한다.

• 무역학회지
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• 제46권1호
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• pp.169-187
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• 2021

This paper analyzes the feasibility of using machine learning and deep learning methods to forecast the income and employment using the strategic industries as well as investment, export, and exchange rates. The decision tree, artificial neural network, support vector machine, and deep learning models were used to forecast the income and employment in Busan. The following were the main findings of the comparison of their predictive abilities. First, the decision tree models predict the income and employment well. The forecasting values for the income and employment appeared somewhat differently according to the depth of decision trees and several conditions of strategic industries as well as investment, export, and exchange rates. Second, since the artificial neural network models show that the coefficients are somewhat low and RMSE are somewhat high, these models are not good forecasting the income and employment. Third, the support vector machine models show the high predictive power with the high coefficients of determination and low RMSE. Fourth, the deep neural network models show the higher predictive power with appropriate epochs and batch sizes. Thus, since the machine learning and deep learning models can predict the employment well, we need to adopt the machine learning and deep learning models to forecast the income and employment.