[2604.02577] ROMAN: A Multiscale Routing Operator for Convolutional Time Series Models

Computer Science > Machine Learning arXiv:2604.02577 (cs) [Submitted on 2 Apr 2026] Title:ROMAN: A Multiscale Routing Operator for Convolutional Time Series Models Authors:Gonzalo Uribarri View a PDF of the paper titled ROMAN: A Multiscale Routing Operator for Convolutional Time Series Models, by Gonzalo Uribarri View PDF HTML (experimental) Abstract:We introduce ROMAN (ROuting Multiscale representAtioN), a deterministic operator for time series that maps temporal scale and coarse temporal position into an explicit channel structure while reducing sequence length. ROMAN builds an anti-aliased multiscale pyramid, extracts fixed-length windows from each scale, and stacks them as pseudochannels, yielding a compact representation on which standard convolutional classifiers can operate. In this way, ROMAN provides a simple mechanism to control the inductive bias of downstream models: it can reduce temporal invariance, make temporal pooling implicitly coarse-position-aware, and expose multiscale interactions through channel mixing, while often improving computational efficiency by shortening the processed time axis. We formally analyze the ROMAN operator and then evaluate it in two complementary ways by measuring its impact as a preprocessing step for four representative convolutional classifiers: MiniRocket, MultiRocket, a standard CNN-based classifier, and a fully convolutional network (FCN) classifier. First, we design synthetic time series classification tasks that isolate c...