> Given the lack of forecasting signal, the obvious next step then is to seek out external (exogenous) features in the real world that can help prediction models.

this seems right to me. maybe another interesting approach would be a fusion llm+ts model that does multiple-input-single-output with input metadata and causality narrative. so it "thinks" about what data it has and how predictive it may be of the target variable and when something "interesting" occurs it uses the big priors to synthesize a good guess at what it would look like.

so you'd have something like the time series data plus textual narratives of the causality stories as the training data.