【摘要】 针对填补时间序列中的缺失值通常依赖于已有数据的预测，由于时间序列的复杂性和不确定性导致预测结果常存在误差的问题，为保证数据填补效果，提出一种基于泛化中心聚类的时间序列缺失数据填补方法.首先，计算对象与类之间、类与类之间的距离，量化数据点与聚类中心之间的相对位置关系，得到数据间的空间关系.其次，利用信息瓶颈算法对空间中的泛化中心进行聚类处理，将含有缺失数据的时间序列数据集划分到同一类中.最后，计算簇半径，对泛化中心聚类后产生的离群点数据再次进行可用、弱可用随机损坏数据划分，设置波动阈值，将位于波动阈值内的随机损坏数据与聚类中统一属性值进行字符串对比，实现时间序列缺失数据填补.实验结果表明，该方法在聚类过程中有较高的标准化互信息和命中率，在缺失数据填补时，可保证数据补齐率在80%以上，说明该方法可有效改善时间序列数据的完整性.更多还原

【Abstract】 Aiming at the problem that the filling of missing values in time series usually relied on the predictions of existing data, and the complexity and uncertainty of time series often led to errors in the prediction results. In order to ensure the effectiveness of data filling, we proposed a time series missing data filling method based on generalized center clustering. Firstly, we calculated the distance between objects and classes, as well as between classes, quantified the relative positional relationship between data points and cluster centers, and obtained the spatial relationship between data. Secondly, we used information bottleneck algorithms to cluster the generalization centers in space, dividing time series datasets containing missing data into the same class. Finally, we calculated the cluster radius, divided the outlier data generated by the generalized center clustering into usable and weakly usable randomly damaged data, set a fluctuation threshold, and compared the randomly damaged data within the fluctuation threshold with a string of the unified attribute values in the cluster, achieving the missing data filling in the time series. The experimental results show that this method has high standardized mutual information and hit rate in the clustering process, and can ensure a data replenishment rate of over 80% when filling in missing data, indicating that this method can effectively improve the integrity of time series data.更多还原