Graph-Temporal Probabilistic Forecasting with Interpolation-Aware Data Representation

This study proposes a method for probabilistic time-series forecasting on graphs incorporating interpolation (IA-GTPF)—a decision-tree-based ensemble approach that combines observation quality metrics, historical load dynamics, information on neighboring nodes, residual correction, quantile regression, and conformal calibration. The method’s performance was evaluated using the Open Power System Data Household dataset. Fixed time boundaries defined distinct periods for training, validation, calibration, and testing. Only information available at the time of forecasting was used as input, while graph weights and preprocessing parameters were determined solely based on the training set. A separate calibration period was used to adjust confidence interval boundaries without altering point forecasts. On the main test set, the IA-GTPF method demonstrated the following metrics for a 15-min forecast horizon: a Mean Absolute Error (MAE) of 0.9824 kW, a Root Mean Square Error (RMSE) of 2.5408 kW, and a Coefficient of Determination (R2) of 0.98947. For the 60-min horizon, the corresponding values were 1.3940 kW, 4.2017 kW, and 0.97122. The QuantileForest model showed a lower MAE for the 15-min horizon (0.9695 kW); however, IA-GTPF achieved the lowest RMSE and highest R2 values. For the 60-min horizon, the IA-GTPF method demonstrated the lowest MAE and RMSE values among all evaluated models.