A Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attention
This addresses pattern recognition in sequence data for applications like time series analysis, but it appears incremental as it combines existing techniques.
The paper tackled pattern mining in complex sequence data by integrating Bidirectional LSTM with multi-scale attention, and it outperformed existing models in accuracy, precision, and recall on a multivariate time series dataset.
This paper addresses the challenges of mining latent patterns and modeling contextual dependencies in complex sequence data. A sequence pattern mining algorithm is proposed by integrating Bidirectional Long Short-Term Memory (BiLSTM) with a multi-scale attention mechanism. The BiLSTM captures both forward and backward dependencies in sequences, enhancing the model's ability to perceive global contextual structures. At the same time, the multi-scale attention module assigns adaptive weights to key feature regions under different window sizes. This improves the model's responsiveness to both local and global important information. Extensive experiments are conducted on a publicly available multivariate time series dataset. The proposed model is compared with several mainstream sequence modeling methods. Results show that it outperforms existing models in terms of accuracy, precision, and recall. This confirms the effectiveness and robustness of the proposed architecture in complex pattern recognition tasks. Further ablation studies and sensitivity analyses are carried out to investigate the effects of attention scale and input sequence length on model performance. These results provide empirical support for structural optimization of the model.