From News to Explainable Forecast: Commonsense, Causal, and Logic-Consistent Reflection for LLM Forecasting

Incorporating external news into numerical time-series forecasting is critical for capturing exogenous shocks that historical data alone cannot explain. However, existing LLM-based iterative forecasters generate reflections that merely identify missing news categories, lacking causal reasoning about their impact or plausibility checks, which makes the refinement loop prone to spurious correlations and slow convergence. We address this by introducing an explainable reflection framework with three structural constraints: a causal chain linking missing news to prediction errors, a commonsense sanity check, and an explicit logic consistency constraint against prior reflections. We then train an Explainable Process Reward Model (EPRM) that scores candidate news articles by jointly optimizing for expected error reduction and the above rational quality. Only candidates passing a quality gate are incorporated. Experiments on electricity, bitcoin, traffic, and exchange-rate benchmarks show that our method achieves a 10.5% average RMSE reduction over the unconstrained iterative baseline. Furthermore, on bitcoin, the spurious-item rate falls from 40.3% to 4.0%, and the method receives high automatic-judge scores for causal, commonsense, and logic quality, as well as high expert scores on these dimensions and on usefulness.