Complete Implementation of EMD-DBN for Time Series Signal Prediction

EMD-DBN is a time series prediction method that combines Empirical Mode Decomposition (EMD) with Deep Belief Network (DBN). This approach enhances prediction accuracy by decomposing signals and leveraging deep learning models, making it suitable for analyzing high-frequency or non-stationary signals in finance, healthcare, industrial monitoring, and other domains. The program logic consists of three main phases: Signal Decomposition Phase The EMD algorithm adaptively decomposes the original time series signal into multiple Intrinsic Mode Functions (IMFs), effectively separating different frequency components and addressing challenges in non-stationary signal processing. In implementation, the EMD algorithm typically involves iterative sifting processes to extract IMFs through local extrema detection and envelope fitting. Feature Learning Phase Each IMF component serves as input to the DBN, where deep features are extracted through pre-training and fine-tuning processes. The stacked Restricted Boltzmann Machine (RBM) architecture of DBN excels at capturing nonlinear temporal dependencies. Code implementation involves configuring RBM layers with appropriate hidden units and implementing contrastive divergence for weight updates. Prediction and Reconstruction Phase Prediction results from individual IMF components are linearly superimposed to generate the final reconstructed signal. Testing datasets typically contain samples with known fluctuation patterns to validate model performance in trend prediction and anomaly detection. The reconstruction process involves summing predicted IMF values while maintaining temporal alignment. Key advantages of this method include EMD's flexibility in handling nonlinear and non-stationary signals, complemented by DBN's powerful feature learning capabilities. Practical applications require attention to mode mixing issues and hyperparameter optimization strategies, such as adjusting the number of IMFs, RBM layer sizes, and learning rates through cross-validation techniques.