Next Article in Journal
Reconstruction of the Radar Reflectivity of Convective Storms Based on Deep Learning and Himawari-8 Observations
Next Article in Special Issue
Modal Parameters Identification of Bridge Structures from GNSS Data Using the Improved Empirical Wavelet Transform
Previous Article in Journal
Rupture Kinematics and Coseismic Slip Model of the 2021 Mw 7.3 Maduo (China) Earthquake: Implications for the Seismic Hazard of the Kunlun Fault
Previous Article in Special Issue
Estimating the Fractional Cycle Biases for GPS Triple-Frequency Precise Point Positioning with Ambiguity Resolution Based on IGS Ultra-Rapid Predicted Orbits
Article

A New Multi-Scale Sliding Window LSTM Framework (MSSW-LSTM): A Case Study for GNSS Time-Series Prediction

by 1, 2,*, 2 and 2
1
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
2
GNSS Research Center, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editor: Nicola Cenni
Remote Sens. 2021, 13(16), 3328; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163328
Received: 1 August 2021 / Revised: 16 August 2021 / Accepted: 21 August 2021 / Published: 23 August 2021
GNSS time-series prediction plays an important role in the monitoring of crustal plate movement, and dam or bridge deformation, and the maintenance of global or regional coordinate frames. Deep learning is a state-of-the-art approach for extracting high-level abstract features from big data without any prior knowledge. Moreover, long short-term memory (LSTM) networks are a form of recurrent neural networks that have significant potential for processing time series. In this study, a novel prediction framework was proposed by combining a multi-scale sliding window (MSSW) with LSTM. Specifically, MSSW was applied for data preprocessing to effectively extract the feature relationship at different scales and simultaneously mine the deep characteristics of the dataset. Then, multiple LSTM neural networks were used to predict and obtain the final result by weighting. To verify the performance of MSSW-LSTM, 1000 daily solutions of the XJSS station in the Up component were selected for prediction experiments. Compared with the traditional LSTM method, our results of three groups of controlled experiments showed that the RMSE value was reduced by 2.1%, 23.7%, and 20.1%, and MAE was decreased by 1.6%, 21.1%, and 22.2%, respectively. Our results showed that the MSSW-LSTM algorithm can achieve higher prediction accuracy and smaller error, and can be applied to GNSS time-series prediction. View Full-Text
Keywords: deep learning; long short-term memory; multi-scale sliding window; GNSS; time series; prediction deep learning; long short-term memory; multi-scale sliding window; GNSS; time series; prediction
Show Figures

Figure 1

MDPI and ACS Style

Wang, J.; Jiang, W.; Li, Z.; Lu, Y. A New Multi-Scale Sliding Window LSTM Framework (MSSW-LSTM): A Case Study for GNSS Time-Series Prediction. Remote Sens. 2021, 13, 3328. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163328

AMA Style

Wang J, Jiang W, Li Z, Lu Y. A New Multi-Scale Sliding Window LSTM Framework (MSSW-LSTM): A Case Study for GNSS Time-Series Prediction. Remote Sensing. 2021; 13(16):3328. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163328

Chicago/Turabian Style

Wang, Jian, Weiping Jiang, Zhao Li, and Yang Lu. 2021. "A New Multi-Scale Sliding Window LSTM Framework (MSSW-LSTM): A Case Study for GNSS Time-Series Prediction" Remote Sensing 13, no. 16: 3328. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163328

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop