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.
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References
Willmott, C.J., Matsuura, K.: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim. Res. 30(1), 79–82 (2005)
Sun, B., et al.: Correcting and complementing freeway traffic accident data using mahalanobis distance based outlier detection. Techn. Gaz. 24(5), 1597–1607 (2017)
Sun, B., et al.: Securing 6G-enabled IoT/IoV networks by machine learning and data fusion. EURASIP J. Wirel. Commun. Netw. 1, 1–17 (2022)
Sun, B., Ma, L., Shen, T., Geng, R., Zhou, Y., Tian, Y.: A robust data-driven method for muti-seasonal and heteroscedastic IoT time series preprocessing. Wirel. Commun. Mob. Comput. (WCMC) 2021(6692390), 1–11 (2021)
Wang, T., et al.: Missing value filling based on the collaboration of cloud and edge in artificial Intelligence of Things. IEEE Trans. Ind. Informat. 18(8), 5394–5402 (2021)
Fatlawi, H.K., Kiss, A.: An adaptive classification model for predicting epileptic seizures using cloud computing service architecture. Appl. Sci. 12(7), 3408 (2022)
Yen, N.Y., et al.: Analysis of interpolation algorithms for the missing values in IoT time series: a case of air quality in Taiwan. J. Supercomput. 76(8), 6475–6500 (2020)
Velasco-Gallego, C., Lazakis, I.: A novel framework for imputing large gaps of missing values from time series sensor data of marine machinery systems. Ships Offshore Struct. 17(8), 1802–1811 (2022)
Bogaerts, T., et al.: A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data. Transp. Res. Part C Emerg. Technol. 112, 62–77 (2020)
Baggag, A., et al.: Learning spatiotemporal latent factors of traffic via regularized tensor factorization: imputing missing values and forecasting. IEEE Trans. Knowl. Data Eng. 33(6), 2573–2587 (2019)
Che, Z., et al.: Recurrent neural networks for multivariate time series with missing values. Sci. Rep. 8(1), 1–12 (2018)
Bergmeir, C., BenÃtez, J.M.: On the use of cross-validation for time series predictor evaluation. Inf. Sci. 191, 192–213 (2012)
Rahman, A., Srikumar, V., Smith, A.D.: Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks. Appl. Energy 212, 372–385 (2018)
Sun, B., et al.: Dynamic emergency transit forecasting with IoT sequential data. Mob. Netw. Appl., 1–15 (2022)
Sun, B., et al.: Prediction of emergency mobility under diverse IoT availability. EAI Endorsed Trans. Pervasive Health Technol. 8(4), e2 (2022)
Tan, H., et al.: A tensor-based method for missing traffic data completion. Transp. Res. Part C Emerg. Technol. 28, 15–27 (2013)
Habtemichael, F.G., Cetin, M.: Short-term traffic flow rate forecasting based on identifying similar traffic patterns. Transp. Res. Part C Emerg. Technol. 66, 61–78 (2016)
Li, L., et al.: Missing value imputation for traffic-related time series data based on a multi-view learning method. IEEE Trans. Intell. Transp. Syst. 20(8), 2933–2943 (2018)
Xiao, Y., Shao, H., Han, S., Huo, Z., Wan, J.: Novel joint transfer network for unsupervised bearing fault diagnosis from simulation domain to experimental domain. IEEE-ASME Trans. Mechatron. 27(6), 5254–5263 (2022)
Chen, M., Shao, H., Dou, H., Li, W., Liu, B.: Data augmentation and intelligent fault diagnosis of planetary gearbox using ILoFGAN under extremely limited samples. IEEE Trans. Reliab. (2022)
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Chen, X., Sun, B., Bi, S., Yang, J., Wang, Y. (2024). IoT Time-Series Missing Value Imputation - Comparison of Machine Learning Methods. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 535. Springer, Cham. https://doi.org/10.1007/978-3-031-50580-5_37
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DOI: https://doi.org/10.1007/978-3-031-50580-5_37
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