自适应动力学模型的时间序列主趋势分析

Xue-bo Jin, Nian-Xiang Yang, Tingli Su, Jianlei Kong
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引用次数: 1

摘要

对于时间序列数据的高频波动,许多应用都需要提取和分析其主趋势。本文重点对卡尔曼滤波的主要趋势进行了分析,给出了提取模型和变换模型,并讨论了保证系统收敛的关键参数的合适取值。利用估计结果分析了主趋势的高次动态特性。仿真结果表明,该方法能够有效地提取时间序列数据的主趋势,并能准确地解释主趋势的特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Time-Series Main Trend Analysis by Adaptive Dynamics Model
For high-frequency fluctuations of time-series data, it is necessary to extract and analyze the main trend for many applications. This paper focuses on the main trend analysis by Kalman filter, gives the extraction model and the transform model, and discusses the suitable value for the key parameters to guarantee the system convergence. The high order dimensional dynamics of the main trend are analyzed by the estimate results. The simulations show that the developed method is effective for extracting the main trend of the time-series data and able to explain accurately the characteristics of the main trend.
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