Feature-Based Reservoir of Dynamic Subspaces for Data Streams Under Intermediate Latency

Abstract

Data streams present significant challenges to predictive modelling, most notably due to their inherent non-stationarity. This means that their underlying distribution may change over time, a phenomenon known as concept drift. Concept drift represents a major hurdle for classification models, as they require recent labelled instances to adapt to the new concept effectively. While existing literature addresses concept drift, the vast majority assumes that true labels are available immediately after inference (the null latency scenario). However, in many real-world applications, this assumption is overly optimistic. For instance, when predicting tomorrow’s weather, the true label is only available after an unavoidable delay. This scenario is known as intermediate verification latency. In such environments, delayed labels make it difficult to detect drift in a timely manner, potentially degrading model performance. To address these challenges, we propose a new classification model called Feature-based Reservoir of Dynamic Subspaces (FeRDS). Our experimental results demonstrate that FeRDS delivers superior prediction performance across various datasets and latency levels while maintaining low memory consumption and processing time.