Streaming predictions need not reread history: ReMAQ turns model-averaging updates into one quadratic program per batch
In real-time prediction over streaming data, an investigator often holds several generalized linear models at once, while model averaging is typically a convex optimization over all historical data. ReMAQ replaces that procedure with a quadratic approximation, so when a new batch arrives the averaged prediction is updated sequentially by solving one quadratic program, without accessing the entire history. Under regularity conditions its weights are asymptotically equivalent to oracle model averaging built on the inaccessible full data, which establishes asymptotic optimality; simulation studies and an application to the Airline On-Time Performance Dataset assess the method.