Release v1.2.4: Operational API Standardization

Added

• API Standardization: Introduced explicit fit() and predict() methods across all meta-models (AdaptiveTAM, OperaTAM, KalmanTAM). This establishes a unified, scikit-learn-like operational workflow (train on historical data, freeze state, predict out-of-sample) regardless of the underlying algorithm.
• AdaptiveTAM: Added fit() and predict() methods for production deployment. The fit() method efficiently extracts and solves the linear system strictly for the final available training window. The predict() method then applies this frozen state (last_state_dict_) to new data in $O(1)$ time with strict safety clipping, ensuring instant, deterministic inference without target leakage.
• KalmanTAM: Added fit() and predict() methods alongside end-of-training state extraction (last_state_dict_ and scale_dict_). This allows users to project the finalized Kalman drift weights forward as a stable, static rule on new data, with the internal normalization math handled automatically.
• OperaTAM: Added fit() and predict() methods to transition from continuous dynamic simulation to frozen-weight inference. fit() runs the historical simulation, while predict() cleanly extracts and applies the final expert aggregation weights to new out-of-sample data.

Fixed

• StaticTAM: Removed the target_col requirement from the required features check in decompose_prediction. This resolves a critical blocker for operational inference pipelines where the target variable is naturally unavailable.
• KalmanTAM: Patched _prepare_kalman_features to securely bypass target column extraction during out-of-sample inference, preventing crashes when the target variable is absent.