A New MultiScale Sliding Window LSTM Framework (MSSWLSTM): A Case Study for GNSS TimeSeries Prediction
Abstract
:1. Introduction
2. Methodology
2.1. LSTM
2.2. MultiScale Sliding Window LSTM
2.3. Evaluation Criteria
3. Data and Processing Strategy
3.1. MSSWLSTM Process Strategy
3.2. MSSWLSTM Processing for the XJSS Station
 The fixed sliding window length was 10, and the predicted length was 1;
 The fixed sliding window length was 15, and the predicted length was 1;
 The fixed sliding window length was 20, and the predicted length was 1;
4. Experimental Result and Analysis
4.1. Experiment Results of Three Networks
4.2. Experiment Summary
5. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Step  Description 

1  The GNSS coordinate time series is obtained by actual observation or solution, which should have dimensional consistency, such as weeks, days, hours, seconds.

2  The new subsequence is constructed by MSSW. Each subsequence can be divided into training and validation datasets.

3  The subLSTM network is constructed for each corresponding data set, and the constructed network is trained and saved separately. In practice, to reduce running time and computing space, the network model should be simple and practical.

4  Training and preserving network structures The final prediction result is obtained by weighted summation.

SubSequence (1)  SubSequence (2)  SubSequence (3)  

Total  990  985  980 
Training  696  691  686 
Validation  294  294  294 
SubLSTM(1)  SubLSTM(2)  SubLSTM(3)  

Layers  1  1  1 
Hidden Cells  10  15  20 
Learning Rate  0.01  0.01  0.01 
Train Window  10  15  20 
Predict Window  1  1  1 
Epochs  5000  5000  5000 
RMSE  MAE  
LSTM(1)  3.2292  2.4253 
LSTM(2)  4.1434  3.0239 
LSTM(3)  3.9810  3.0679 
MSSWLSTM  3.1628  2.3864 
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Share and Cite
Wang, J.; Jiang, W.; Li, Z.; Lu, Y. A New MultiScale Sliding Window LSTM Framework (MSSWLSTM): A Case Study for GNSS TimeSeries Prediction. Remote Sens. 2021, 13, 3328. https://doi.org/10.3390/rs13163328
Wang J, Jiang W, Li Z, Lu Y. A New MultiScale Sliding Window LSTM Framework (MSSWLSTM): A Case Study for GNSS TimeSeries Prediction. Remote Sensing. 2021; 13(16):3328. https://doi.org/10.3390/rs13163328
Chicago/Turabian StyleWang, Jian, Weiping Jiang, Zhao Li, and Yang Lu. 2021. "A New MultiScale Sliding Window LSTM Framework (MSSWLSTM): A Case Study for GNSS TimeSeries Prediction" Remote Sensing 13, no. 16: 3328. https://doi.org/10.3390/rs13163328