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This study introduces a hybrid deep learning technique for complex time series data. The method enhances denoising and prediction accuracy for real-time sequence analysis, outperforming traditional approaches.

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Area of Science:

  • Complex Systems Analysis
  • Signal Processing
  • Machine Learning

Background:

  • Complex systems exhibit strong randomness and non-stationarity, posing challenges for accurate time series prediction.
  • Traditional prediction methods often struggle with noise and inherent variability in real-time data.

Purpose of the Study:

  • To develop a hybrid multi-strategy prediction technique integrating optimized denoising and deep learning for complex systems.
  • To enhance the accuracy and reliability of real-time sequence data prediction.

Main Methods:

  • Optimized Variational Mode Decomposition (VMD) using Sparrow Search Algorithm (SSA) to decompose signals into Intrinsic Mode Functions (IMFs).
  • Wavelet Soft Threshold (WST) denoising applied to IMF components based on Pearson Correlation Coefficient (PCC) for signal reconstruction.
  • A hybrid deep learning model combining Convolutional Neural Network (CNN) and Bidirectional Short-Term Memory (BiLSTM) for trend analysis and prediction.

Main Results:

  • The optimized SSA-VMD-WST method achieved the highest Signal-to-Noise Ratio (SNR) and Correlation Coefficient (CC) (20.2383 and 0.9342, respectively).
  • The proposed CNN-BiLSTM-SSA-VMD-WST model demonstrated superior performance with the lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) (0.150 and 0.188), and the highest R-squared value (0.9364).

Conclusions:

  • The CNN-BiLSTM-SSA-VMD-WST method is highly effective for denoising and predicting real-time sequence data.
  • This hybrid approach offers a more reliable alternative to traditional and singular deep learning methods for complex system predictions across various industries.