Beyond Forecasting: Memory-Augmented Time Series Anomaly Prediction

Time series anomaly prediction (TSAP) aims to anticipate future anomalous states from historical observations for early warning and proactive decision-making. Existing point-level methods typically infer anomaly states from forecasted futures, yet forecasting favors the most likely, normal-dominated evolution, while anomaly prediction depends on preserving rare but discriminative abnormal dynamics. This mismatch often leads to over-smoothed futures and weakened point-level anomaly signals. To bridge this gap, we propose LAMP, a lightweight memory-augmented framework that complements parametric prediction with non-parametric evidence from previously observed future evolutions. Given a historical window, LAMP retrieves futures associated with similar historical patterns from a multi-scale memory and adaptively integrates them with parametric predictions, allowing TSAP to exploit not only what is likely to happen, but also what actually happened after similar histories. Experiments on five multivariate time-series benchmarks against 16 baselines show that LAMP consistently improves anomaly prediction, achieving average gains of 6.71% in V-PR and 9.37% in V-ROC, while training up to 8.2× faster with 14.0× lower GPU memory usage than forecasting-based alternatives. Code is available at https://anonymous.4open.science/r/lamp/.