基于时间序列分析的多尺度数据融合算法设计

Chunxia Wang
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引用次数: 0

摘要

时间序列是指在不同时间对不同数值的指示,按时间顺序排列。多尺度分析的基本思想是通过正交变换,以及小波变换等信号在不同尺度上的分解分析。时序分析方法是通过模型法实现的。动态数据时域分析方法的过程参数是对观测数据进行参数化拟合,然后利用该模型对观测数据进行分析,生成数据系统。提出了一种基于时间序列分析的多尺度数据融合算法。最后从估计精度和仿真验证了新算法的有效性两方面阐述了新算法的优点。
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The Design of the Multi-Scale Data Fusion Algorithm Based on Time Series Analysis
Time series is an indicator at different times on different values, arranged in chronological sequence. The basic idea of the multi-scale analysis by orthogonal transformation, and it is such as wavelet transform signal decomposition analysis on different scales. The timing analysis method is achieved through the model method. The process parameters of the dynamic data time-domain analysis method is a parametric model to fit the observed data, and then use this model to analyze the observational data and produce data system. The paper presents the design of the multi-scale data fusion algorithm based on time series analysis. Finally, the advantages of the new algorithm are elaborated from the estimation accuracy and simulation demonstrated the effectiveness of the new algorithm.
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