Signal data often contains missing values. Effective replacement (imputation) of the missing values can have significant positive effects on processing the signal. In this paper, we compare three commonly employed methods for estimating missing values in time series data: forward fill, backward fill, and mean fill. We carry out a large scale experimental analysis using 3,600 AR(1)-based simulated time series to determine the optimal method for estimating missing values. The results of the numerical experiments show that the forward and backward fill methods are better suited for times series with large positive correlations, while the mean fill method is better suited for times series with low or negative correlations. The extensive and exhaustive nature of the numerical experiments provides a definitive answer to the comparison of the three imputation methods.
翻译:信号数据通常包含缺失值。 有效替换( 估计) 缺失值可能对处理信号产生显著的积极影响。 在本文中, 我们比较了三种常用的方法来估计时间序列数据中的缺失值: 前填、 后填和 中填。 我们用 3 600 AR(1) 模拟时间序列来进行大规模实验分析, 以确定缺失值的最佳估算方法。 数字实验的结果显示, 前填和后填方法更适合具有大正对应关系的时间序列, 而中填方法更适合低或负对应关系的时间序列。 数字实验的广泛性和详尽性为比较三种估算方法提供了明确答案。