Research Article
IoT Time-Series Missing Value Imputation - Comparison of Machine Learning Methods
@INPROCEEDINGS{10.1007/978-3-031-50580-5_37, author={Xudong Chen and Bin Sun and Shuhui Bi and Jiafeng Yang and Youling Wang}, title={IoT Time-Series Missing Value Imputation - Comparison of Machine Learning Methods}, proceedings={Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part IV}, proceedings_a={ICMTEL PART 4}, year={2024}, month={2}, keywords={Time series Missing values Machine learning Imputation}, doi={10.1007/978-3-031-50580-5_37} }- Xudong Chen
Bin Sun
Shuhui Bi
Jiafeng Yang
Youling Wang
Year: 2024
IoT Time-Series Missing Value Imputation - Comparison of Machine Learning Methods
ICMTEL PART 4
Springer
DOI: 10.1007/978-3-031-50580-5_37
Abstract
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.
