RELIABILITY-DRIVEN STREAMING MODELLING OF MULTIMODAL TIME SERIES FOR ROBUST DECISION SUPPORT UNDER DRIFT AND MODALITY DEGRADATION - Uzun, I. S.; Lobachev, M. V. (Systems and Technologies, 2026)
Streaming decision support systems that process multimodal time series must remain robust under simultaneous concept drift and temporary modality degradation. Existing approaches usually treat multimodal fusion, drift adaptation, anomaly detection, and probability calibration as separate problems, which makes it difficult to distinguish whether a loss of predictive quality is caused by a real change in the process or by a temporary failure of one modality. This paper presents a unified online pipeline in which online reliability estimation is used as a single control interface for reliability-adaptive dynamic fusion, driftinitiated budgeted micro-adaptation, and reliability-constrained anomaly detection under a false alarm rate budget. Reliability is modeled as a causal probability of the current non-degraded modality state, post-hoc calibrated, and reused in all downstream control rules. Evaluation follows the prequential protocol on controlled streams with deterministic injections of modality degradation, concept drift, and anomalies, and on real data from UCI Appliances Energy Prediction and UCI Air Quality. The calibrated reliability model retains high degradation-separation ability (ROC - AUC = 0.8624) and improves calibration to ECE = 0.0845 versus 0.1840 without calibration. RADF preserves clean-regime quality (MAE = 0.5557; RMSE = 0.6952) and improves degraded segments, for example MAE = 0.6402 versus 0.6820 for early fusion under alternating missingness. Budgeted micro-adaptation improves post-drift forecasting relative to no adaptation (MAE = 0.6613 versus 0.7046; average recovery 157 versus 800 steps) while updating only a three-parameter head within fixed budgets. RC-AD increases Recall@FAR at all tested budgets, including 0.335 versus 0.103 at a false alarm rate budget of 0.05 on controlled streams. In the integrated stress scenario, the system achieves ROC - AUC = 0.983, ECE = 0.029, Recall@FAR=0.311, and PR - AUC = 0.287; in robust application protocols with severe segment missingness, it reduces MAE by about 35 % in the energy domain and about 93 % in the BTC/USD domain relative to naive fusion. These results present one coherent streaming decision support result rather than isolated local methods.