channel selection

Channel Selection refers to the process in time series data processing where algorithms are used to select a subset of channels that are most representative and informative from multiple channels, in order to optimize model performance and reduce computational resource consumption. The goal is to enhance the efficiency and accuracy of feature extraction, ensuring that the model can better capture key patterns in the data. In multi-channel time series analysis, appropriate channel selection can significantly improve the robustness and generalization ability of the system, while also reducing the risk of overfitting. This technique is widely applied in signal processing, biomedical engineering, and financial data analysis, playing a crucial role in the efficient handling and precise prediction of complex data.