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NTIS 바로가기응용통계연구 = The Korean journal of applied statistics, v.32 no.6, 2019년, pp.795 - 835
정혜린 (중앙대학교 응용통계학과) , 임창원 (중앙대학교 응용통계학과)
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 ...
핵심어 | 질문 | 논문에서 추출한 답변 |
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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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