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신승준,서범석 한국통계학회 2024 응용통계연구 Vol.37 No.1
최근 코로나19 등으로 경제 불확실성이 확대됨에 따라 민간 경제주체의 경제상황을 직접적으로 반영하는 민간소비 동향을 신속히 파악할 필요성이 높아지고 있다. 이에 본 연구는 기존 거시경제지표 뿐만 아니라 빅데이터를 종합적으로 활용하여 민간소비를 실시간으로 추정(nowcasting)하는 방법을 제안하였다. 특히 초고차원 빅데이터의 적합을 위해 활용 가능한 다양한 기계학습 방법론을 비교분석하여 민간소비 추정의 정확도를 향상시키고자 하였다. 실증 분석 결과, 빅데이터를 비롯한 가용 공변량의 수가 많은 경우에는 변수를 미리 선별하여 모형적합에 활용하는 것이 민간소비 예측 성능을 향상시킬 수 있음을 확인하였다. 또한 코로나19 이후 빅데이터의 반영이 민간소비 예측 성능을 더욱 크게 향상시킴에 따라 경제 불확실성이 높은 상황일수록 새로운 정보를 적시에 반영할 수 있는 고빈도 빅데이터의 활용가치가 높은 것으로 판단된다. As economic uncertainties have increased recently due to COVID-19, there is a growing need to quickly grasp private consumption trends that directly reflect the economic situation of private economic entities. This study proposes a method of estimating private consumption in real-time by comprehensively utilizing big data as well as existing macroeconomic indicators. In particular, it is intended to improve the accuracy of private consumption estimation by comparing and analyzing various machine learning methods that are capable of fitting ultra-high-dimensional big data. As a result of the empirical analysis, it has been demonstrated that when the number of covariates including big data is large, variables can be selected in advance and used for model fit to improve private consumption prediction performance. In addition, as the inclusion of big data greatly improves the predictive performance of private consumption after COVID-19, the benefit of big data that reflects new information in a timely manner has been shown to increase when economic uncertainty is high.
스마트공장을 위한 빅데이터 애널리틱스 플랫폼 아키텍쳐 개발
신승준,우정엽,서원철 한국멀티미디어학회 2016 멀티미디어학회논문지 Vol.19 No.8
While global manufacturing is becoming more competitive due to variety of customer demand, increase in production cost and uncertainty in resource availability, the future ability of manufacturing industries depends upon the implementation of Smart Factory. With the convergence of new information and communication technology, Smart Factory enables manufacturers to respond quickly to customer demand and minimize resource usage while maximizing productivity performance. This paper presents the development of a big data analytics platform architecture for Smart Factory. As this platform represents a conceptual software structure needed to implement data-driven decision-making mechanism in shop floors, it enables the creation and use of diagnosis, prediction and optimization models through the use of data analytics and big data. The completion of implementing the platform will help manufacturers: 1) acquire an advanced technology towards manufacturing intelligence, 2) implement a cost-effective analytics environment through the use of standardized data interfaces and open-source solutions, 3) obtain a technical reference for time-efficiently implementing an analytics modeling environment, and 4) eventually improve productivity performance in manufacturing systems. This paper also presents a technical architecture for big data infrastructure, which we are implementing, and a case study to demonstrate energy-predictive analytics in a machine tool system.