文献类型：期刊文献

英文题名：A Time-series Prediction Algorithm Based on a Hybrid Model

作者：Cao, Danyang[1,2]; Ma, Jinfeng[1]; Sun, Ling[3,4]; Ma, Nan[5]

第一作者：Cao, Danyang

机构：[1] School of Information Science and Technology, North China University of Technology, Beijing, 100144, China; [2] Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, Beijing, 100144, China; [3] National Satellite Meteorological Center, Beijing, 100081, China; [4] Key Laboratory of Radiometric Calibration and Validation for Environmental Satellite, Beijing, 100081, China; [5] College of Robotics, Beijing Union University, Beijing, 100101, China

第一机构：School of Information Science and Technology, North China University of Technology, Beijing, 100144, China

年份：2023

卷号：16

期号：1

起止页码：3-17

外文期刊名：Recent Advances in Computer Science and Communications

收录：EI(收录号：20224713141857);Scopus(收录号：2-s2.0-85142040556)

语种：英文

外文关键词：Calibration - Forecasting - Long short-term memory

摘要：Background: In reality, time series is composed of several basic components, which have linear, nonlinear and non-stationary characteristics at the same time. Directly using a single model will show some limitations and the prediction accuracy is difficult to improve. Methods: We propose a mixed forecasting model based on time series decomposition, namely STL-EEMD-LSTM model. First, we use STL filtering algorithm to decompose the time series to obtain the trend component, seasonal component and the remainder component of the time series; then we use EEMD to decompose the seasonal component and the remainder component to obtain multiple sub-sequences. After this, we reconstruct the new seasonal component and the remainder component according to the fluctuation frequency of the sub-sequence. Finally, we use LSTM to build a prediction model for each component obtained by decomposition. Results: We applied the proposed model to simulation data and the time series of satellite calibration parameters and found that the hybrid prediction model proposed in this paper has high prediction accuracy. Conclusion: Therefore, we believe that our proposed model is more suitable for the prediction of time series with complex components. ? 2023 Bentham Science Publishers.