首页 | 本学科首页   官方微博 | 高级检索  
     

时间序列预测的发展与应用
引用本文:乐 天,蔡远文,马雪松,王 莉.时间序列预测的发展与应用[J].兵工自动化,2015,34(2):63-68.
作者姓名:乐 天  蔡远文  马雪松  王 莉
作者单位:装备学院研究生管理大队,北京,101416;装备学院航天装备系,北京,101416;装备学院基础系,北京,101416
摘    要:为了给运载火箭测试数据预测问题的研究打下基础,开辟研究思路,对时间序列预测方法进行研究。介绍了传统时间序列预测方法中的指数平滑法、季节模型和求和回归滑动平均模型的发展,重点阐述了时间序列预测的3类热门研究领域:多变量时间序列模型、模糊时间序列模型和组合预测模型,并通过文献介绍,分析了时间序列预测技术的应用与改进方向。

关 键 词:预测  时间序列分析  多变量时间序列  模糊时间序列  组合预测模型
收稿时间:2015/3/30 0:00:00

Development and Application of Time Series Forecasting
Le Tian;Cai Yuanwen;Ma Xuesong;Wang Li.Development and Application of Time Series Forecasting[J].Ordnance Industry Automation,2015,34(2):63-68.
Authors:Le Tian;Cai Yuanwen;Ma Xuesong;Wang Li
Affiliation:Le Tian;Cai Yuanwen;Ma Xuesong;Wang Li;Administrant Brigade of Postgraduate, Academy of Equipment;Department of Spaceflight Equipment, Academy of Equipment;Department of Fundamental Courses, Academy of Equipment;
Abstract:In order to lay a foundation for the data forecasting of the rocket launch test and open up some ideas, the time series forecast methods are researched. The development of exponential smooth, seasonal method and autoregressive integrated moving average model, which are part of the traditional time series forecasting method, are introduced at first. And then three hot areas of time series forecasting are introduced, which is multivariate time series model, fuzzy time series mode and combination forecasting model. The future direction of application and improvement of time series forecasting are analyzed through the introduction of literatures.
Keywords:forecasting  time series analysis  multivariate time series  fuzzy time series  combination forecasting model
本文献已被 CNKI 万方数据 等数据库收录!
点击此处可从《兵工自动化》浏览原始摘要信息
点击此处可从《兵工自动化》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号