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인공지능 기반 수요예측 기법의 리뷰
A review of artificial intelligence based demand forecasting techniques 원문보기

응용통계연구 = The Korean journal of applied statistics, v.32 no.6, 2019년, pp.795 - 835  

정혜린 (중앙대학교 응용통계학과) ,  임창원 (중앙대학교 응용통계학과)

초록
AI-Helper 아이콘AI-Helper

최근 다양한 분야에서 '빅데이터'가 생성되었다. 많은 기업들은 인공지능(AI)을 기반으로 빅데이터 분석이 가능한 시스템을 구축하여 이익 창출을 시도하고 있다. 인공지능 기술을 접목함으로써 방대한 양의 데이터를 효율적으로 분석하고 효과적으로 활용하는 것은 점점 더 중요해지고 있다. 특히 재무, 조달, 생산 및 마케팅과 같은 다양한 분야에서 국가 및 기업 경영 관리에있어 최소의 오차와 최대의 정확도를 갖춘 수요예측은 절대적으로 중요한 요소이다. 이때 각 분야의 수요패턴을 고려한 적절한 모델을 적용하는 것이 중요하다. 전통적으로 쓰이는 시계열모델이나 회귀모델로도 비대해진 실제 데이터의 복잡한 비선형적인 패턴을 분석할 수 있다. 그러나 다양한 비선형 모델들 중에서 적절한 모델을 선택하는 것은 사전 지식 없이는 어려운 일이다. 최근에는 인공지능 기반의 기법들인 머신러닝이나 딥러닝 기법을 중심으로 이루어진 연구들이 이를 극복할 수 있음을 증명하고 있다. 뿐만 아니라 정형데이터와 이미지나 텍스트의 비정형 데이터 분석을 통한 수요예측도 높은 정확도를 갖춘 결과를 보이고 있다. 따라서 본 연구에서는 수요예측이 비교적 활발하게 일어나는 중요한 분야들을 나누어 설명하였다. 그리고 각 분야별로 갖는 특징적인 성격을 고려한 인공지능 기반의 수요예측 기법에 대해 머신러닝과 딥러닝 기법으로 나누어 소개하였다.

Abstract AI-Helper 아이콘AI-Helper

Big data has been generated in various fields. Many companies have now tried to make profits by building a system capable of analyzing big data based on artificial intelligence (AI) techniques. Integrating AI technology has made analyzing and utilizing vast amounts of data increasingly valuable. In ...

주제어

표/그림 (15)

질의응답

핵심어 질문 논문에서 추출한 답변
회귀분석법은 무엇인가? 시계열분석법은 일정한 시간 간격을 두고 기록된 데이터를 바탕으로 미래를 예측하며 대표적으로 autoregressive (AR), moving average (MA), autoregressive integrated moving average (ARIMA), exponential smoothing (지수평활법) 등이 있다. 회귀분석법은 단일 또는 다수의 독립변수들과 종속변수 간의 관계를 찾기 위해 관 계식을 추정하고, 추정된 관계식으로 독립변수들을 통해 미래를 예측하는 방법이다. 또 수요가 거의 없 거나 전혀 없는 새로운 부품에 대한 보정된 예측을 하거나 간헐적인 수요패턴을 보이는 제품의 재고관 리나 생산량에 대한 예측에는 베이지안 모델을 적용하기도 한다 (Bergman 등, 2017).
인공지능기반 수요예측기법의 장점은? 인공지능기반 수요예측기법의 가장 큰 장점은 예측의 정확도와 계산량의 효율성이라 할 수 있다. 기존 수요예측기법에 비해 상대적으로 높은 정확도를 보이며 예측실행시간도 덜 소요되기 때문이다.
RNN구조의 단점인 '장기 의존성' 문제는 무엇을 말하는가? 그러나 기존의 RNN 구조의 단점은 장기 의존성(Long-Term Dependency) 문제이다. 네트워크 구조 상 짧은 시퀀스에 대해서만 효과를 보이기 때문에 시점이 길어질수록 앞의 정보가 뒤로 충분히 전달되 지 못하는 현상이 발생한다. RNN에서 발생하는 가중치 값의 소실이나 포화 문제로 인해 장기간 데이터셋을 훈련하기에 적합하지 않기 때문에 이 문제를 해결하고자 Hochreiter와 Schmidhuber (1997)은 LSTM을 제안하였다.
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