LSTM for Network Traffic Prediction: A Review

Network traffic prediction is a critical component for efficient network management, resource allocation, and anomaly detection. The inherent non-linearity and temporal dependencies in network traffic data make accurate forecasting a challenging task for traditional methods. Deep learning techniques, particularly Long Short-Term Memory (LSTM) networks, have emerged as powerful tools for capturing these complex patterns. This paper provides a comprehensive review of various LSTM algorithm types and their applications in network traffic prediction. We explore standard LSTM architectures and their enhancements, including temporal LSTMs, bidirectional LSTMs, stacked LSTMs, convolutional LSTMs, parallel LSTMs, LSTMs with attention, LSTMs incorporating autocorrelation, LSTMs with wavelet transformation, real-time updating LSTMs, and multivariate LSTMs. For each type, we discuss their underlying principles and their effectiveness in addressing specific characteristics of network traffic. This review aims to provide a structured overview of the state-of-the-art in LSTM-based network traffic prediction, highlighting their advantages and potential for future research.