Feature Extraction of Time Series Data Based on CNN-CBAM

Methods for extracting features from time series data using deep learning have been widely studied, but they still suffer from problems of severe loss of feature information across different network layers and parameter redundancy. Therefore, a new time-series data feature extraction model (CNN-CBAM) that integrates convolutional neural networks (CNN) and convolutional attention mechanisms (CBAM) is proposed. First, the parameters of the CNN and BiGRU prediction models are optimized through uniform design methods. Next, the CNN is used to extract features from the time series data, outputting multiple feature maps. These feature maps are then subjected to feature re-extraction by the CBAM attention mechanism at both the spatial and channel levels. Finally, the feature maps are input into the BiGRU model for prediction. Experimental results show that after CNN-CBAM processing, the stability and accuracy of the BiGRU prediction model improved by 77.6% and 76.3%, respectively, outperforming other feature extraction methods. Meanwhile, the training time of the model has only increased by 7.1%, demonstrating excellent time efficiency.