Why Us? >>

  • - Open Access
  • - Peer-reviewed
  • - Rapid publication
  • - Lifetime hosting
  • - Free indexing service
  • - Free promotion service
  • - More citations
  • - Search engine friendly

Free SCIRP Newsletters>>

Add your e-mail address to receive free newsletters from SCIRP.

 

Contact Us >>

WhatsApp  +86 18163351462(WhatsApp)
   
Paper Publishing WeChat
Book Publishing WeChat
(or Email:book@scirp.org)

Article citations

More>>

Zhu, S.H. and You, C.X. (2013) A Modified Average Filtering Algorithm. Computer Applications and Software, 12, 97-99. (In Chinese)

has been cited by the following article:

  • TITLE: Criteria for Weighted Moving-Mean Method

    AUTHORS: Shuo Jiang, Jinliang Wang

    KEYWORDS: Weighted Moving-Mean, Least Square Method, Extreme-Point Symmetric Mode Decomposition Method, Auto Regressive Moving-Mean, Data Analysis Methods

    JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.7 No.9, September 9, 2019

    ABSTRACT: The moving-mean method is one of the conventional approaches for trend-extraction from a data set. It is usually applied in an empirical way. The smoothing degree of the trend depends on the selections of window length and weighted coefficients, which are associated with the change pattern of the data. Are there any uniform criteria for determining them? The present article is a reaction to this fundamental problem. By investigating many kinds of data, the results show that: 1) Within a certain range, the more points which participate in moving-mean, the better the trend function. However, in case the window length is too long, the trend function may tend to the ordinary global mean. 2) For a given window length, what matters is the choice of weighted coefficients. As the five-point case concerned, the local-midpoint, local-mean and global-mean criteria hold. Among these three criteria, the local-mean one has the strongest adaptability, which is suggested for your usage.