Exploiting Wavelet Recurrent Neural Networks for satellite telemetry data modeling, prediction and control
作者：
Highlights：
• The model combines RNN and the Wavelet Transform into a unique neural network.
• Exploits the multiple correlated features of satellite telemetries for prediction.
• Overcomes traditional analytical and numerical models, that are more expensive.
• Yields multi-steps ahead medium-term forecasts with an high accuracy.
• Outperforms the simple RNN in terms of accuracy and width of the forecast horizon.
摘要
•The model combines RNN and the Wavelet Transform into a unique neural network.•Exploits the multiple correlated features of satellite telemetries for prediction.•Overcomes traditional analytical and numerical models, that are more expensive.•Yields multi-steps ahead medium-term forecasts with an high accuracy.•Outperforms the simple RNN in terms of accuracy and width of the forecast horizon.
论文关键词：Time series forecast,Multivariate time series,Wavelet analysis,LSTM,wavelet recurrent neural networks
论文评审过程：Received 15 December 2021, Revised 26 May 2022, Accepted 8 June 2022, Available online 18 June 2022, Version of Record 5 July 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117831
