Univariate imputation, such as Kalman filtering, is not able to provide a reasonable imputation for a variable when periods of missing values are large. A new method is needed that can provide feasible imputations in such scenarios. We propose a novel method of applying multivariate imputation in combination with an existing univariate imputation approach to a single variable in an LTE spectrum dataset, such as the average cell throughput, by exploiting the high weekly seasonality of this variable. Performance comparison shows that our proposed method significantly outperforms Kalman filtering in terms of imputation accuracy.