Abstract
This work analyzes time series and find the rules and statistical characteristics from the numerous data. According to the purpose of the time series analysis, we find the rules and conduct the future time forecast. This paper is mainly based on the similarity of time series. Based on clustering results, XGB is used to reflect the relationship between similarity and clusters’ weights and to predict the value. Overall, it is a time series prediction model based on clustering and XGB regulated weights. The process of model prediction is realized by using instances in dataset, and the relationship between similarity and weights is obtained by using XGB.
This work is supported by Shandong Key R&D Program grant 2019JZZY021005.
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Sun, H., Zhang, K., Wang, T., Ma, W., Zhao, Q. (2022). Clustering-XGB Based Dynamic Time Series Prediction. 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_25
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