# The multi-strategy hybrid forecasting base on SSA-VMD-WST for complex system

## 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.

## Key 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 time sequence data. Finally, we applied the CNN-BiLSTM-SSA-VMD-WST to predict the real time sequence data together with the other methods in order to prove it’s effectiveness. The results show that SNR and CC of the SSA-VMD-WST are the largest (the values are 20.2383 and 0.9342). The performance of the CNN-BiLSTM-SSA-VMD-WST are the best, MAE and RMSE are the smallest (which are 0.150 and 0.188), the goodness of fit R2 is the highest(its value is 0.9364). In contrast with other methods, CNN-BiLSTM-SSA-VMD-WST method is more suitable for denoising and prediction of real time series data than the traditional and singular deep learning methods. The proposed method may provide a reliable way for related prediction in various industries.