IoT Time-Series Missing Value Imputation - Comparison of Machine Learning Methods

Data about time series has been researched for ages in various fields. In past few years, with the advancements of the Internet of Things (IoT) and the use of data acquisition devices, more and more time series data are being provided. However, due to the failure of the data acquisition equipment, some data is lost, and these lost data may contain important information. In order to deal with these lost data, many different machine learning algorithms have appeared, such as K-NN, CNN, random forest, etc. The purpose of this work is to compare the effects of two diverse models, K-NN and Random Forest on missing values imputation which is in traffic data, and to evaluate the two models, the root mean square error (RSTM) [1] index is adopted.