אופטימיזציה של חלונות נסיגה עם חישוב ממוצע: תוספת זמן-תחום פשוטה למחזור ירושלים

תקציר מקורי באנגליתarXiv:2604.09067v2 Announce Type: replace Abstract: Augmentation has become a central technique for improving deep forecasting models, but classification-style transformations tend to break the coherence between the look-back window and its continuous future target. We describe a simple procedure that unfolds the joint input-target sequence into overlapping sliding windows, randomly reorders a controlled fraction of them-prioritized by a lightweight variance criterion-and reconstructs the sequence by averaging across the overlaps, producing synthetic samples with controlled variation while limiting temporal distortion. The procedure is model-agnostic, introduces only three interpretable hyperparameters, and achieves strong improvements over a comprehensive set of competing augmentations ac