FastODT: A tree-based framework for efficient continual learning

ArXi:2603.13276v1 Announce Type: cross Machine learning models deployed in real-world settings must operate under evolving data distributions and constrained computational resources. This challenge is particularly acute in non-stationary domains such as energy time series, weather monitoring, and environmental sensing. To remain effective, models must adaptability, continuous learning, and long-term knowledge retention. This paper