Fourier Series Fitting Method of Multiple Periodic Time-Series Data
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    Abstract:

    Generally speaking, time-series data can be divided into three ingredients such as trend, random and season. Analyzing and modeling through the available time-series data, change law can find things contained. A kind of high precision data fitting method was designed for multiple periodic time-series data. Firstly, the algorithm eliminated random component of fitted time-series data. Secondly, several co-primed basic cycles were analyzed by the application of the theory of autocorrelation function. Finally, multiple periodic time-series data was fitting by using multiple sets of Fourier series based on the least squares principle. Practical application proved the effectiveness and progressiveness of the algorithm.

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黄雄波.多周期时序数据的傅氏级数拟合算法.计算机系统应用,2015,24(7):142-148

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History
  • Received:November 20,2014
  • Revised:December 30,2014
  • Online: July 17,2015
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