TiNAS: Adapting Time Series Forecasting Architectures with Language Models

The architecture best suited to a forecasting task can vary with its temporal dynamics, variable dependencies, and forecast horizon. Nevertheless, most forecasting models adapt to new tasks by optimizing parameters within a fixed architecture, leaving their internal structure unchanged. We introduce TiNAS, a task-feedback-driven framework that extends forecasting adaptation from parameter estimation to architecture optimization. It preserves the PatchTST representation pathway while searching its encoder at a fine granularity, including normalization placement, attention configuration, local temporal operators, feed-forward design, and depth. An autoregressive LLM controller generates these decisions sequentially by conditioning on the forecasting task, preceding architectural choices, and observed performance, and learns its search policy from validation MSE rewards. A weight-sharing supernet and progressive architecture derivation strategy enable efficient exploration, after which the highest-ranked architectures are independently retrained from scratch and selected by validation performance. Across eight forecasting benchmarks, TiNAS achieves the best average MSE and MAE on four datasets and improves the average MSE of PatchTST on seven. Controlled ablations further confirm the complementary roles of the search space, controller, and search strategy. These results show that task-specific structural adaptation can substantially extend the capability of an established forecasting backbone.