论文标题
Kolmogorov-Zurbenko期刊的理论和实际限制具有动态平滑，以估计信号频率
Theoretical and Practical Limits of Kolmogorov-Zurbenko Periodograms with Dynamic Smoothing in Estimating Signal Frequencies
论文作者
论文摘要
这项研究建立了Kolmogorov-Zurbenko期刊的理论和实际限制，并在信号频率的敏感性，准确性，分辨率和鲁棒性方面对信号频率进行了动态平滑。 dirienzo-zurbenko算法基于周期图中的局部变化执行动态平滑，而Neagu-Zurbenko算法则基于周期图中与线性的局部偏离进行动态平滑。本文以Dirienzo-Zurbenko算法和Neagu-Zurbenko算法的统计基础的摘要开始，然后说明了R统计计划平台中访问和利用这些方法的说明。简短的定义，重要性，统计基础，理论和实际限制以及示范的敏感性，准确性，分辨率和鲁棒性在估计信号频率时。接下来，使用模拟时间序列，其中两个信号接近频率的信号被嵌入在显着的随机噪声中，将这些方法的预测能力与自回归的积分移动平均值（ARIMA）方法进行了比较，并且在数据丢失时再次获得了支持。在整个过程中，文章将Kolmogorov-Zurbenko期刊的限制与动态平滑与静态平滑度的对数期图的限制进行了对比，同时还比较了Direenzo-Zurbenko algorithm的性能与Neagu-Zurbenko algorithm的表现。结束时，通过描述下一步的步骤来确定具有动态平滑估计信号强度的Kolmogorov-Zurbenko周期图的精确度。
This investigation establishes the theoretical and practical limits of Kolmogorov-Zurbenko periodograms with dynamic smoothing in their estimation of signal frequencies in terms of their sensitivity, accuracy, resolution, and robustness. While the DiRienzo-Zurbenko algorithm performs dynamic smoothing based on local variation in a periodogram, the Neagu-Zurbenko algorithm performs dynamic smoothing based on local departure from linearity in a periodogram. This article begins with a summary of the statistical foundations for both the DiRienzo-Zurbenko algorithm and the Neagu-Zurbenko algorithm, followed by instructions for accessing and utilizing these approaches within the R statistical program platform. Brief definitions, importance, statistical bases, theoretical and practical limits, and demonstrations are provided for their sensitivity, accuracy, resolution, and robustness in estimating signal frequencies. Next using a simulated time series in which two signals close in frequency are embedded in a significant level of random noise, the predictive power of these approaches are compared to the autoregressive integral moving average (ARIMA) approach, with support again garnered for their being robust when data is missing. Throughout, the article contrasts the limits of Kolmogorov-Zurbenko periodograms with dynamic smoothing to those of log-periodograms with static smoothing, while also comparing the performance of the DiRienzo-Zurbenko algorithm to that of the Neagu-Zurbenko algorithm. It concludes by delineating next steps to establish the precision with which Kolmogorov-Zurbenko periodograms with dynamic smoothing estimate signal strength.
