Beck Marcus W, Bokde Neeraj, Asencio-Cortés Gualberto, Kulat Kishore
USEPA National Health and Environmental Effects Research Laboratory, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, FL 32651, USA.
Visvesvaraya National Institute of Technology, Nagpur, North Ambazari Road, Nagpur, India.
R J. 2018;10(1):218-233.
Missing observations are common in time series data and several methods are available to impute these values prior to analysis. Variation in statistical characteristics of univariate time series can have a profound effect on characteristics of missing observations and, therefore, the accuracy of different imputation methods. The package can be used to compare the prediction accuracy of different methods as related to the amount and type of missing data for a user-supplied dataset. Missing data are simulated by removing observations completely at random or in blocks of different sizes depending on characteristics of the data. Several imputation algorithms are included with the package that vary from simple replacement with means to more complex interpolation methods. The testbench is not limited to the default functions and users can add or remove methods as needed. Plotting functions also allow comparative visualization of the behavior and effectiveness of different algorithms. We present example applications that demonstrate how the package can be used to understand differences in prediction accuracy between methods as affected by characteristics of a dataset and the nature of missing data.
缺失观测值在时间序列数据中很常见，并且在分析之前有几种方法可用于插补这些值。单变量时间序列统计特征的变化会对缺失观测值的特征产生深远影响，从而影响不同插补方法的准确性。该软件包可用于比较不同方法的预测准确性，这些方法与用户提供的数据集中缺失数据的数量和类型有关。通过根据数据特征完全随机地或按不同大小的块删除观测值来模拟缺失数据。该软件包包含几种插补算法，从简单的均值替换到更复杂的插值方法不等。测试平台不限于默认函数，用户可以根据需要添加或删除方法。绘图函数还允许对不同算法的行为和有效性进行比较可视化。我们展示了示例应用程序，这些应用程序演示了如何使用该软件包来理解受数据集特征和缺失数据性质影响的不同方法之间预测准确性的差异。
