TY - CONF T1 - Effective Missing Value Imputation Methods for Building Monitoring Data T2 - IEEE BigData 2020 Y1 - 2020/12// A1 - Brian Cho A1 - Teresa Dayrit A1 - Yuan Gao A1 - Zhe Wang A1 - Tianzhen Hong A1 - Alex Sim A1 - Kesheng KW - building monitoring KW - imputation KW - interpolation KW - Matrix factorization AB - To understand behaviors of natural and man-made events, such as energy consumption of buildings, which ac-counts for 40% of energy uses in the US, we deploy automated monitoring devices to record periodic observations. However, such experimental and observation data often contains prob-lems and irregularities that have to be cleaned up before analyses. Due to various conditions affecting sensor operations, the communication channels, recording steps, or the recording media, the recorded data might have missing values, errors, or anomalous values. An effective way to clean up these problems is to replace these missing values, errors and anomalous values with expected values, a process generally known as imputation. In this work, we survey commonly used missing value impu-tation techniques and compare their performance on a set of building monitoring data. To compare the different types of sensor measurements with widely varying characteristics, we use normalized root mean squared error (NRMSE) as the key metric for the effectiveness of the imputation methods. We ad-ditionally consider periodicity and run time when considering comparing methods. Through extensive testing, we ﬁnd that for small gap sizes, up to 8 consecutive missing values, linear interpolation performs the best; for larger gaps stretching up to 48 consecutive missing values, K-nearest neighbors provides the most accurate imputations; for even larger gaps, more computational intensive methods, such as matrix factorization, achieve the smallest NRMSE. Additionally, we observe that these computationally intensive algorithms not only provide accurate imputations for large gaps, but are also more robust across all types of sensors. JF - IEEE BigData 2020 DO - 10.1109/BigData50022.2020.9378230 ER -