דיוק תחזית: לא תמיד זה קורא לרווח: פיתוח רשתות חוזרות קטנות לתחזית תפוצה של רווחי מניה

תקציר מקורי באנגליתarXiv:2610.07825v1 Announce Type: cross Abstract: Time series forecasting models are typically compared on pointwise error, which scores a prediction in isolation from the decision it is produced for, and a lower forecast error does not imply a better decision downstream. A parallel debate asks whether modern transformer architectures forecast better than recurrent and other lightweight models. We compare linear, fixed recurrent, transformer, and mixing based architectures against recurrent networks evolved by neuroevolutionary architecture search, evaluating each on forecast accuracy and on the net return of a daily long/short strategy. All models are fit on a pooled panel, one network trained across the whole universe. Across four mid-cap portfolios and three trading years, the evolved n