Noise analysis of unevenly spaced time series data
Noise analysis of unevenly spaced time series data
复制标题
不均匀间隔时间序列数据的噪声分析
DOI：
10.1088/0026-1394/33/5/4
复制
发表时间：
1996
期刊：
影响因子：
2.4
通讯作者：
T. Parker
中科院分区：
文献类型：
--
作者：
C. Hackman;T. Parker
双向卫星时间和频率传输 (TWSTFT) 数据通常在周一、周三和周五记录。这会产生间隔不均匀的时间序列，很难对其执行准确的两样本方差分析。我们研究了不均匀数据间距对 σx(τ) 计算的影响 [1-2]。为从白相位调制到随机游走频率调制的噪声过程生成均匀间隔的模拟数据集。然后计算每种噪声类型的 σx(τ)。随后从每个模拟数据集中删除数据，以创建两个间隔不均匀的数据集，平均间隔为 2.8 和 3.6 天。这些稀疏集对应于典型的 TWSTFT 数据模式。然后使用两种不同的方法计算每个稀疏数据集的 σx(τ)。首先，缺失的数据点被线性插值替换，并根据现在完整的数据集计算出 σx(τ)。第二种方法忽略了数据间隔不均匀的事实，并计算 σx(τ)，就好像数据间隔均匀，平均间隔为 2.8 或 3.6 天。不均匀的数据间距对这两种方法的结果的影响是显着的，并且进行了讨论。最后，提出了纠正模拟 TWSTFT 数据集中数据间距不均匀引起的误差的技术，并将适当的技术应用于实际数据集。
Two-way satellite time and frequency transfer (TWSTFT) data are typically recorded on Monday, Wednesday and Friday. This produces an unevenly spaced time series on which it is difficult to perform an accurate two-sample variance analysis. We have investigated the effect of uneven data spacing on the computation of σx(τ) [1-2]. Evenly spaced simulated data sets were generated for noise processes ranging from white phase modulation to random walk frequency modulation. σx(τ) was then calculated for each noise type. Data were subsequently removed from each simulated data set to create two unevenly spaced sets with average intervals of 2,8 and 3,6 days. These sparse sets correspond to typical TWSTFT data patterns. σx(τ) was then calculated for each sparse data set using two different approaches. First, the missing data points were replaced by linear interpolation and σx(τ) calculated from this now full data set. The second approach ignored the fact that the data were unevenly spaced and calculated σx(τ) as if the data were equally spaced with average spacing of 2,8 or 3,6 days. The impact of uneven data spacing on the results of these two approaches is significant and is discussed. Finally, techniques are presented for correcting errors caused by uneven data spacing in simulated TWSTFT data sets, and the appropriate technique is applied to a real data set.