Abstract
Due to the rapid development of modern information engineering, a lot of data are used in machine learning and data cleaning and data mining of the hot research fields, such as a large portion of the data algorithm and related data model are built for complete data set, But in our real life and work, the absence of data exists in a large number of data collection, collation, transmission, storage and other links, it causes many obstacles and difficulties to build a model for complete data. The general way of dealing with missing values for simple delete, that deal with missing value method is a simple convenient but can cause: two aspects of the problem and the inconvenience caused by the original data set to reduce, reduce the reliability of the data, especially in the case of data loss is bigger, can cause a large number of data sets to reduce and missing, This has caused a lot of trouble to our work and research, so we need to find a more efficient and better method than direct deletion. In order to better solve the above problems, we mainly fill in the missing values of time series data, which has become an urgent problem to be solved. In this paper, mean filling, median filling, mode filling, PCA-EM filling and other methods are used to fill traffic data. By comparing these methods, the filling effect of each method is evaluated.
This work is supported by Shandong Key R&D Program grant 2019JZZY021005.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Fisher, R.A., Yates, F.: Statistical Tables: For Biological, Agricultural and Medical Research. Oliver and Boyd (1938)
Ma, L., Sun, B., Li, Z.: Bagging likelihood-based belief decision trees. In: 20th International Conference on Information Fusion (FUSION), Xi’an, China, 1–6 (2017). http://ieeexplore.ieee.org/abstract/document/8009664/
Geng, R., Sun, B., Ma, L., Zhao, Q., Shen, T.: Anomaly-aware in sequence data based on MSM-H with EXPoSE. In: 40th Chinese Control Conference (CCC 2021), Shanghai, China (2021)
Batista, G.E., Monard, M.C.: An analysis of four missing data treatment methods for supervised learning. Appl. Artif. Intell. 17(5–6), 519–533 (2003)
Sun, B., Cheng, W., Ma, L., Goswami, P.: Anomaly-aware traffic prediction based on automated conditional information fusion. In: International Conference on Information Fusion (FUSION), Cambridge, United Kingdom, pp. 2283–2289. IEEE (2018)
Leduc, G.: Road traffic data: collection methods and applications. In: Working Papers on Energy, Transport and Climate Change, vol. 1, no. 55, pp. 1–55 (2008)
Sun, B., Cheng, W., Bai, G., Goswami, P.: Correcting and complementing freeway traffic accident data using mahalanobis distance based outlier detection. Tehnicki Vjesnik Tech. Gazette 24(5), 1597–1607 (2017)
Scheffer, J.: Dealing with missing data (2002)
Lv, Y., Duan, Y., Kang, W., et al.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2014)
Ma, L., Sun, B., Han, C.: Learning decision forest from evidential data: the random training set sampling approach. In: 4th International Conference on Systems and Informatics (ICSAI), Hangzhou, China (2017)
Little, R.J.A., Rubin, D.B.: Statistical Analysis with Missing Data. Wiley, Hoboken (2019)
Sun, B., Cheng, W., Goswami, P., Bai, G.: An overview of parameter and data strategies for K-nearest neighbours based short-term traffic prediction. In: ACM International Conference Proceeding Series 2017, pp. 68–74. ACM (2017)
Marlin, B.: Missing Data Problems in Machine Learning (2008)
Sun, B., Ma, L., Shen, T., et al.: A robust data-driven method for muti-seasonal and heteroscedastic IoT time series preprocessing. In: Wireless Communications and Mobile Computing (WCMC), p. 6692390 (2021)
Yu, L., Snapp, R.R., Ruiz, T., et al.: Probabilistic principal component analysis with expectation maximization (PPCA-EM) facilitates volume classification and estimates the missing data. J. Struct. Biol. 171(1), 18–30 (2010)
Sun, B., Cheng, W., Goswami, P., et al.: Short-term traffic forecasting using self-adjusting k-nearest neighbours. IET Intell. Transp. Syst. 12(1), 41–48 (2018)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Geng, R., Li, M., Sun, M., Wang, Y. (2022). Comparing Methods of Imputation for Time Series Missing Values. In: Wang, S., Zhang, Z., Xu, Y. (eds) IoT and Big Data Technologies for Health Care. IoTCare 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 415. Springer, Cham. https://doi.org/10.1007/978-3-030-94182-6_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-94182-6_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-94181-9
Online ISBN: 978-3-030-94182-6
eBook Packages: Computer ScienceComputer Science (R0)