RouteCast: Instance-Dependent Multi-View Routing for Time Series Forecasting

Time-series foundation models (TSFMs) primarily operate on numerical sequences, yet the same observed history can be represented through alternative views that expose different pretrained inductive biases. However, the usefulness of these derived views need not be uniform across forecasting instances, making static multi-view integration potentially suboptimal. We introduce RouteCast, a parameter-efficient framework that formulates multi-view time-series forecasting as an instance-dependent view relevance problem. RouteCast separates view inference from view utilization: Context Distillation constructs instance-conditioned representations from visual and textual views derived solely from the observed history, while Context Routing dynamically regulates their relative influence on a frozen TSFM. All pretrained backbones remain frozen, with training restricted to lightweight adaptation modules. Across 12 forecasting benchmarks, RouteCast consistently improves over its underlying TSFM backbone and remains competitive with strong recent and controlled multi-view baselines. With Chronos as the backbone, RouteCast outperforms full fine-tuning on 11 of 12 datasets, while analogous backbone studies with Timer and TimesFM show similar improvements. Ablations further show complementary benefits from instance-conditioned distillation and adaptive routing. These results demonstrate the value of regulating alternative views according to their instance-dependent relevance for parameter-efficient TSFM adaptation.