Adaptive MTS Forecasting with LSTM-ADWIN for Concept Drift Detection

Introduction: This study addresses the challenge of concept drift in multivariate time series (MTS) forecasting, where data distributions evolve, degrading model performance. The objective is to propose an adaptive hybrid model that combines Long Short-Term Memory (LSTM) networks with the ADaptive WINdowing (ADWIN) algorithm for effective drift detection and adaptive forecasting in real-time environments. Methods: The proposed ADWIN-LSTM framework integrates Bi-LSTM layers for temporal sequence modelling and ADWIN for monitoring prediction residuals to detect concept drift. A dynamic sliding window mechanism adjusts the training data scope based on drift type. Data preprocessing includes normalization, STL-based detrending, and PCA for dimensionality reduction. Hyperparameters are optimized using grid search. Results: Experiments on real-world and synthetic MTS datasets demonstrate that the proposed model outperforms baseline models (LSTM, GRU, CNN-LSTM, and ADWIN-RF) across RMSE, MAE, MAPE, and R² metrics. The model detects both abrupt and gradual drifts with minimal false positives and low detection delay (≤ 5 steps). Post-drift adaptation significantly improves forecasting accuracy. Discussion: The adaptive retraining strategy triggered by drift detection ensures computational efficiency and robustness in volatile environments. The dynamic integration of forecasting and drift detection enhances model adaptability to evolving data distributions. Conclusion: The ADWIN-LSTM framework effectively combines predictive learning and realtime drift adaptation, making it suitable for dynamic, high-stakes environments such as energy, traffic, and environmental systems. Future work includes online optimization and deployment on streaming platforms.