인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
논문을 무제한 열람 이용할 수 있어요.
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.6
- 수록면
- 198 - 208 (11page)
- DOI
- 10.33162/JAR.2024.6.24.2.198
이용수
초록· 키워드
Purpose: This study was conducted to develop an adaptive demand forecasting system that flexibly reevaluates and applies forecasting models according to demand changes, focusing on high-performing forecasting model groups to enhance the demand forecasting accuracy for the main battle tank (K-MBT).
Methods: Twenty-one types of time-series models and machine learning/deep learning models were selected for evaluation. Statistical methods were then applied to select a group of 10 high-performing models. Subsequently, the prediction accuracies of all the 21 initially selected models were compared with the results obtained using 10 high-performing models.
Results: The approach of selecting few high-performing models (10 models) for demand forecasting, compared to using many models (21 models), proved to be superior in terms of accuracy.
Conclusion: For ensuring the operational readiness of weapon systems through the forecast of spare parts demand, we anticipate the continuous enhancement and development of adaptive demand forecasting systems, along with their practical applications.
상세정보 수정요청해당 페이지 내 제목·저자·목차·페이지Methods: Twenty-one types of time-series models and machine learning/deep learning models were selected for evaluation. Statistical methods were then applied to select a group of 10 high-performing models. Subsequently, the prediction accuracies of all the 21 initially selected models were compared with the results obtained using 10 high-performing models.
Results: The approach of selecting few high-performing models (10 models) for demand forecasting, compared to using many models (21 models), proved to be superior in terms of accuracy.
Conclusion: For ensuring the operational readiness of weapon systems through the forecast of spare parts demand, we anticipate the continuous enhancement and development of adaptive demand forecasting systems, along with their practical applications.
정보가 잘못된 경우 알려주세요!
목차
- 1. 서론
- 2. 이론적 고찰 및 문헌연구
- 3. 연구절차 및 연구결과
- 4. 결론
- References
참고문헌
참고문헌 신청최근 본 자료
UCI(KEPA) : I410-151-24-02-089929746
