Abstract We present a method for improving short-term forecasting of the SOne340RM environmental sensor by applying a Java-based Hierarchical Data Transformation (HDT) pipeline and ensemble learning. Using streaming "today" data and prediction horizons from 15 to 90.9 minutes, we implement online feature extraction, temporal aggregation, and lightweight model updates to reduce mean absolute error (MAE) and latency for near-real-time applications. Experiments on a recorded SOne340RM dataset show MAE reductions of 8–18% versus baseline autoregressive models, with update latency under 200 ms on a modern laptop.
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