The multi-strategy hybrid forecasting base on SSA-VMD-WST for complex system
Huiqiang Su, Shaojuan Ma, Xinyi Xu
TL;DR
This paper introduces a new hybrid forecasting method combining denoising and deep learning to improve predictions for complex systems.
Contribution
A novel hybrid forecasting method using SSA-optimized VMD, WST denoising, and a CNN-BiLSTM model for complex system prediction.
Findings
The SSA-VMD-WST method achieved the highest SNR (20.2383) and CC (0.9342) for signal denoising.
The CNN-BiLSTM-SSA-VMD-WST model outperformed others with the lowest MAE (0.150) and RMSE (0.188), and highest R² (0.9364).
The proposed method is more effective for real time series prediction than traditional and singular deep learning approaches.
Abstract
In view of the strong randomness and non-stationarity of complex system, this study suggests a hybrid multi-strategy prediction technique based on optimized hybrid denoising and deep learning. Firstly, the Sparrow search algorithm (SSA) is used to optimize Variational mode decomposition (VMD) which can decompose the original signal into several Intrinsic mode functions (IMF). Secondly, calculating the Pearson correlation coefficient (PCC) between each IMF component and the original signal, the subsequences with low correlation are eliminated, and the remaining subsequence are denoised by Wavelet soft threshold (WST) method to obtain effective signals. Thirdly, on the basis of the above data noise reduction and reconstruction, our proposal combines Convolutional neural network (CNN) and Bidirectional short-term memory (BiLSTM) model, which is used to analyze the evolution trend of real…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26
Figure 27
Figure 28
Figure 29
Figure 30
Figure 31
Figure 32
Figure 33
Figure 34
Figure 35
Figure 36
Figure 37
Figure 38
Figure 39
Figure 40
Figure 41
Figure 42Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMachine Fault Diagnosis Techniques · Energy Load and Power Forecasting · Fault Detection and Control Systems