Time-series Instruction Following: An Endogenous Control Language

Time-series forecasters face changing phase, trend, and shared channel activity even when the prediction task stays fixed. We study whether this state can be expressed as instructions that remain useful when the predictor changes. Our *endogenous instruction language* separates message generation from forecasting response. A compiler maps the observed window and horizon to shared and optional channel-aligned numerical messages; architecture-specific readers learn to use them through joint forecast training. Across seven backbones, the value of instructions depends on the information accessible to the base prediction path. Capacity-matched comparisons establish the predictive value of multichannel content beyond local adaptation. Fixed-weight interventions show that trained predictors use the supplied content, and held-out probes recover phase, trend, and volatility from FITS messages. A frozen learned compiler generates messages for new windows while a new backbone and reader learn their forecasting response. Reusing a FITS compiler with DLinear reduces mean squared error by 12.66% relative to a frozen-random compiler on a new sensor dataset, with all five seeds improving. A horizon-aware DLinear reader also uses the ordered compiler message tokens generated by PatchTST, reducing MSE by 7.56% over a frozen-random compiler across five seeds. Native forecaster configurations also achieve lower mean squared error than official-code iTransformer reproductions across 12 datasets. These results identify learned state expression as a reusable part of forecasting knowledge.