Sone340rmjavhdtoday015909 Min Better (Limited ●)

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.

Introduction
Accurate short-term forecasts of environmental sensor readings enable responsive control systems and real-time alerts. The SOne340RM (hypothetical) is a low-latency sensor producing sub-minute measurements. Challenges include noise, missing samples, and rapid temporal dynamics. We propose a Java-based Hierarchical Data Transformation (HDT) pipeline that performs multiscale aggregation, online normalization, and incremental model updates, optimized for prediction horizons of 15–90.9 minutes.

Methods
Data and preprocessing

HDT pipeline (Java implementation)

Models and evaluation

Results (summary)

Discussion

Practical implementation notes (concise)

Conclusion
A Java-based HDT pipeline with incremental models improves short-term forecasting for SOne340RM-like sensors across 15–90.9 minute horizons, reducing MAE and keeping latency suitable for near-real-time systems. Future work: multi-sensor fusion, outlier-resilient aggregation, and on-device deployment with tighter resource constraints.

References (examples)

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