For volatile multi-metric application performance time series in cloud-native environments, we propose an integrated forecasting and monitoring method and system based on an enhanced Informer. At the encoder side, context-aware reversible normalization (CA-RevIN) is introduced to mitigate distribution shifts, while multi-frequency band convolutional enhancement (MFBCEM) and long-term dependency aggregation (LTA) are used for multi-scale modeling. A generative decoder produces multi-step forecasts. On the system side, a closed-loop architecture is built for data acquisition, feature governance, inference and alerting. Peaks-over-threshold (POT) and adaptive residual thresholds enable hierarchical anomaly detection and early warning. Experiments on public datasetsand real production data show that the proposed approach outperforms LSTM, GRU and the vanilla Informer in multi-step prediction accuracy and stability, while reducing false positives and missed detections.