计算机科学 ›› 2019, Vol. 46 ›› Issue (1): 21-28.doi: 10.11896/j.issn.1002-137X.2019.01.004

• 综述 • 上一篇    下一篇


杨海民1, 潘志松2, 白玮2   

  1. (陆军工程大学研究生院 南京210007)1
    (陆军工程大学指挥控制工程学院 南京210007)2
  • 收稿日期:2018-02-04 出版日期:2019-01-15 发布日期:2019-02-25
  • 作者简介:杨海民(1990-),男,博士生,主要研究方向为机器学习、时间序列预测,E-mail:haiminyang_nj@126.com;潘志松(1973-),男,教授,博士生导师,主要研究方向为机器学习、网络空间安全,E-mail:haiminyang_nj@126.com(通信作者);白 玮(1983-),男,讲师,主要研究方向为网络空间安全。
  • 基金资助:

Review of Time Series Prediction Methods

YANG Hai-min1, PAN Zhi-song2, BAI Wei2   

  1. (School of Graduate,Army Engineering University of PLA,Nanjing 210007,China)1
    (College of Command and Control Engineering,Army Engineering University of PLA,Nanjing 210007,China)2
  • Received:2018-02-04 Online:2019-01-15 Published:2019-02-25

摘要: 时间序列是按照时间排序的一组随机变量,它通常是在相等间隔的时间段内依照给定的采样率对某种潜在过程进行观测的结果。时间序列数据本质上反映的是某个或者某些随机变量随时间不断变化的趋势,而时间序列预测方法的核心就是从数据中挖掘出这种规律,并利用其对将来的数据做出估计。针对时间序列预测方法,着重介绍了传统的时间序列预测方法、基于机器学习的时间序列预测方法和基于参数模型的在线时间序列预测方法,并对未来的研究方向进行了进一步的展望。

关键词: 时间序列, 时间序列预测, 机器学习, 在线学习

Abstract: Time series is a set of random variables ordered in timestamp.It is often the observation of an underlying process,in which values are collected from uniformly spaced time instants,according to a given sampling rate.Time series data essentially reflects the trend that one or some random variables change with time.The core of time series prediction is mining the rule from data and making use of it to estimate future data.This paper emphatically introduced a summary of time series prediction method,namely the traditional time series prediction method,machine learning based time series prediction method and online time series prediction method based on parameter model,andfurther prospected the future research direction.

Key words: Time series, Time series prediction, Machine learning, Online learning


  • TP181
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