Jing-Yuan Chen

Cloud Resource Workload Forecasting Method Based on the MST-iTransformer Model

The proposed MST-iTransformer model achieves simultaneous reductions in prediction errors and inference latency, validating the effectiveness of the multi-scale temporal encoding, sparse attention mechanism, and adaptive feature selection modules in improving forecasting accuracy and computational efficiency.