Time Series Prediction Application of Deep Learning in Multidimensional Signal Processing

This article explores the application of deep learning techniques for time series prediction in multidimensional signal processing, specifically utilizing long short-term memory networks (LSTM). Traditional time series forecasting methods face many challenges when dealing with high-dimensional and nonlinear data. To address these challenges, this study introduces the basic concepts of time series forecasting, the application of deep learning in this field, and the characteristics and challenges of multidimensional signal processing. We adopt the LSTM model to process multi-dimensional time series data and conduct experimental verification on multiple public datasets. Experimental results show that compared with traditional methods, our method performs well in both prediction accuracy and efficiency. This study not only proposes a new multi-dimensional time series forecasting framework but also experimentally proves the effectiveness and advantages of deep learning technology in this field, providing new insights and methodological guidance for future research in related fields.